Accurate decision-making for complex industrial processes is essential for improving resource utilization and optimizing operating conditions. However, current decision work-flows operate within a hierarchical closed loop between the operations layer and the process-control layer. Cross-layer transmission of objectives and constraints and feedback coupling can degrade performance; the effect is pronounced under nonstationary conditions or when objectives or constraints change. To address these challenges, we propose the Bi-level Evolving Neural Networks (BENNs) framework with four components: (i) a bi-level neural architecture that explicitly models cross-layer information flow and constraint propagation; (ii) a graph neural network-based structural similarity assessment that reduces redundant evaluations to improve search quality; (iii) an online evolving network strategy that adapts rapidly to process dynamics without extensive retraining; and (iv) a convex-hull knee-point-based compromise selection that encodes engineering preferences. Experiments on real mineral processing data and on the Tennessee Eastman Process (TEP) show improvements over baselines in prediction accuracy, performance retention, and adaptation speed, demonstrating the potential of BENNs for complex industrial settings.

Sensor drift, which is the deviation of measurements over time, can compromise controller performance and cause system instability. To address this challenge, this article proposes a proactive fault-tolerant control strategy for distributed parameter systems. The proposed strategy is based on a time-varying spatiotemporal model that captures system dynamics. The initial phase of this research involves designing an adaptive observer-based detector to identify the temporal and spatial locations of fault occurrences accurately. Subsequently, a joint state-and-fault estimator is developed to accurately reconstruct the fault profile, even in the presence of strong state-fault coupling. The controller provides real-time corrections based on the estimation results. A rigorous stability analysis of the closed-loop system is provided, and the effectiveness of the controller is validated through experiments involving two distinct fault scenarios.

Optimization problems in real-world applications often involve dynamic environmental changes, requiring algorithms to adapt quickly, track optimal solutions, and maintain efficiency. Existing dynamic multiobjective optimization evolutionary algorithms (DMOEAs) typically rely on fixed or limited dynamic response mechanisms, which are often insufficient to handle complex and varied dynamic environments. To overcome these limitations, this article proposes an adaptive dynamic response-based DMOEA (ADR-DMOEA), which employs a subpopulation-level adaptive mechanism to coordinate diversity-driven, prediction-driven, and memory-driven strategies. The strategy weights are dynamically adjusted according to the static optimization distance of each subpopulation, ensuring that appropriate strategies are adaptively deployed in different environments. This design overcomes the inefficiency of fixed assignments and the instability of individual-level perturbations, enabling coordinated and stable evolution. Extensive experiments on DF benchmark functions and a blast furnace (BF) ironmaking case study demonstrate that ADR-DMOEA achieves superior convergence, diversity, and robustness compared to state-of-the-art algorithms, effectively supporting real-world decision-making under dynamic conditions.

The remarkable success of reconstruction-based methods in Unsupervised Anomaly Detection (UAD) lies in their ability to identify and localize anomalies by modeling discrepancies between input images and their reconstructed counterparts. However, these approaches often struggle to capture subtle anomalies and tend to produce blurred anomaly boundaries, which significantly limits their effectiveness, particularly in complex multi-class scenarios. To address these issues, we present XMatchAD, a novel UAD framework that reinterprets the task from a pseudo cross-modal matching perspective. Specifically, the input and reconstructed images are treated as two complementary modalities and their matching relationships are precisely exploited for anomaly detection. First, a pre-trained feature extractor is employed to encode discriminative representations. Second, an attention-guided cross-modal matching mechanism is introduced to match local inter-modal anomaly-related patterns while mutually refining the features. This enhances the sensitivity to anomalies with diverse shapes and subtle deviations and significantly improves the precision of anomaly detection and localization. Third, we design an adaptive frequency-aware fusion module that further delineates sharp anomaly boundaries through the coupling of high-frequency components from cross-modal multi-scale representations. Comprehensive evaluations on MVTec-AD, VisA, and MPDD benchmarks demonstrate that our method consistently achieves superior performance, outperforming state-of-the-art methods in multi-class anomaly detection and localization. The code will be released at https://github.com/Mingxiu-Cai/XMatchAD.

Multirate industrial processes pose significant challenges for accurate forecasting due to varying sampling frequencies and missing data. This article proposes a novel hybrid deep learning framework that effectively addresses these issues. Our approach uses a combination of time series decomposition, inverted transformer (iTransformer)-based feature extraction, and a modified minimal gated unit (MGU) network. To handle missing quality variables, we introduce a robust adaptive parameter update algorithm based on dead-zone Kalman filtering. Through extensive experiments conducted on real-world industrial datasets, our method achieves a mean absolute error (MAE) reduction of 61.42%, a root-mean-square error (RMSE) reduction of 64.11%, and a high qualification rate improvement of 14.73% compared to the average performance of state-of-the-art technologies, thereby outperforming existing state-of-the-art techniques in terms of both forecasting accuracy and robustness.

Thermal imaging offers valuable properties, but suffers from inherently low spatial resolution, which can be enhanced using a high-resolution (HR) visible image as guidance. However, the substantial modality differences between thermal and visible images, coupled with significant resolution gaps, pose challenges to existing guided super-resolution (SR) approaches. In this article, we present dual-conditional diffusion (DuaDiff), an innovative diffusion model featuring a dual-conditioning mechanism to enhance guided thermal image SR. Unlike typical conditional diffusion models, DuaDiff integrates a learnable Laplacian pyramid to extract high-frequency details from the visible image, serving as one of the conditioning inputs. By capturing multiscale high-frequency components, DuaDiff effectively focuses on intricate textures and edges in the HR visible images, significantly enhancing thermal image fidelity. Furthermore, we project both thermal and visible images into a semantic latent space, constructing another conditioning input. Leveraging these complementary conditions, DuaDiff employs a multimodal latent feature cross-attention module to facilitate effective interaction between noise, thermal, and visible latent representations. Extensive experiments on the FLIR-ADAS and CATS datasets for $4\times $ and $8\times $ guided SR demonstrate that combining learnable Laplacian conditioning with semantic latent conditioning enables DuaDiff to surpass state-of-the-art methods in both visual quality and metric evaluation, particularly in scenarios with a large resolution gap. Besides, the applications to downstream tasks further confirm the capability of DuaDiff to recover high-fidelity semantic information. The code will be released.

This article presents a novel distributed attack-immune fault detection and identification observer (AI-FDIO) for the detection and isolation of sensor and actuator faults in multiagent systems that operate under an attack-immune consensus protocol and are subject to input disturbances. The protocol relies solely on relative output information between neighboring agents, directly measured by local sensors. Based on this locally acquired information, the AI-FDIO performs sensor and actuator fault detection without requiring interagent communication, thereby reducing vulnerability to cyberattacks. Traditional observer-based methods typically decouple residuals from control inputs, making them ineffective in detecting sensor faults that directly affect control signals. In contrast, the proposed AI-FDIO can effectively decouple sensor fault indicators from actuator faults and input disturbances—and vice versa—without relying on any prior knowledge of the faults. A systematic design methodology is presented, and simulation studies on transport aircraft with short-period dynamics and two-wheeled self-balancing robots demonstrate the effectiveness and robustness of the proposed AI-FDIO.

Biaxial motion control systems are widely employed in manufacturing industries, where improving throughput and reducing machine costs are critical goals. To achieve these, lightweight materials are increasingly being used in structural components, though this often introduces higher flexibility in the machine links, leading to position-dependent precision loss in the end-effector. This article addresses the challenge of maintaining contouring accuracy in such systems by proposing a novel contouring error-bounded control algorithm for biaxial switched linear systems. The algorithm employs model predictive control (MPC) to enforce state, input, and contouring error constraints across different system modes, even when mode switching is not known in advance. While the exact switching signal is unknown, the controller assumes knowledge of the minimum dwell time the system remains in each mode. The proposed algorithm guarantees recursive feasibility and ensures closed-loop system stability. The effectiveness of the method is validated through a high-fidelity simulation of a dual-drive industrial laser machine, demonstrating that the contouring error is consistently maintained within the specified tolerance.

Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstruction-based) or by learning an image feature embedding space (embedding-based), existing approaches fundamentally rely on image-level or feature-level matching to derive anomaly scores. Often, such a matching process is inaccurate yet overlooked, leading to sub-optimal detection. To address this issue, we introduce the concept of cost filtering, borrowed from classical matching tasks, such as depth and flow estimation, into the UAD problem. We call this approach CostFilter-AD. Specifically, we first construct a matching cost volume between the input and normal samples, comprising two spatial dimensions and one matching dimension that encodes potential matches. To refine this, we propose a cost volume filtering network, guided by the input observation as an attention query across multiple feature layers, which effectively suppresses matching noise while preserving edge structures and capturing subtle anomalies. Designed as a generic post-processing plug-in, CostFilter-AD can be integrated with either reconstruction-based or embedding-based methods. Extensive experiments on MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for both single- and multi-class UAD tasks. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.

To address the challenge of implementing 5G wireless networks in industrial process control, this paper integrates deep learning and reinforcement learning in AI with anti-disturbance industrial control to propose an anti-latency optimized control method for 5G wireless networks. The proposed method includes: an anti-latency controller based on data-driven signal compensators with PID controllers and the controller parameters self-optimization algorithm combining digital twin and reinforcement learning. The proposed anti-latency control method is integrated with end-edge-cloud collaboration technologies of Industrial Internet to create an intelligent control system architecture that achieves anti-latency optimized control. A control experimental system based on commercial 5G and Industrial Ethernet was established, and anti-latency control experiments are conducted on an actual heat exchange system. The experimental results indicate that the proposed anti-latency intelligent control method significantly eliminates the impact of 5G random latency on the dynamic performance of the control system.

We present a novel Geometry-aware Neural Interpolation (Geo-NI) framework for light field rendering. Previous learning-based approaches either perform direct interpolation via neural networks, which we dubbed Neural Interpolation (NI), or explore scene geometry for novel view synthesis, also known as Depth Image-Based Rendering (DIBR). Both kinds of approaches have their own strengths and weaknesses in addressing non-Lambert effect and large disparity problems. In this paper, we incorporate the ideas behind these two kinds of approaches by launching the NI within a specific DIBR pipeline. Specifically, a DIBR network in the proposed Geo-NI serves to construct a novel reconstruction cost volume for neural interpolated light fields sheared by different depth hypotheses. The reconstruction cost can be interpreted as an indicator reflecting the reconstruction quality under a certain depth hypothesis, and is further applied to guide the rendering of the final high angular resolution light field. To implement the Geo-NI framework more practically, we further propose an efficient modeling strategy to encode high-dimensional cost volumes using a lower-dimension network. By combining the superiorities of NI and DIBR, the proposed Geo-NI is able to render views with large disparities with the help of scene geometry while also reconstructing the non-Lambertian effect when depth is prone to be ambiguous. Extensive experiments on various datasets demonstrate the superior performance of the proposed geometry-aware light field rendering framework.

Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled and shifted target domain. The majority of existing UDA-SS works typically consider images whilst recent attempts have extended further to tackle videos by modeling the temporal dimension. Although two lines of research share the major challenges - overcoming the underlying domain distribution shift, their studies are largely independent. It causes several issues: (1) The insights gained from each line of research remain fragmented, leading to a lack of holistic understanding of the problem and potential solutions. (2) Preventing the unification of methods and best practices across two scenarios (images and videos) will lead to redundant efforts and missed opportunities for cross-pollination of ideas. (3) Without a unified approach, the knowledge and advancements made in one scenario may not be effectively transferred to the other, leading to suboptimal performance and slower progress. Under this observation, we advocate unifying the study of UDA-SS across video and image scenarios, enabling a more comprehensive understanding, synergistic advancements, and efficient knowledge sharing. To that end, we explore the unified UDA-SS from a general domain augmentation perspective, serving as a unifying framework, enabling improved generalization, and potential for cross-pollination, ultimately contributing to the practical impact and overall progress. Specifically, we propose a Quad-directional Mixup (QuadMix) method, characterized by tackling intra-domain discontinuity, fragmented gap bridging, and feature inconsistencies through four-directional paths designed for intra- and inter-domain mixing within an explicit feature space. To deal with temporal shifts within videos, we incorporate optical flow-guided feature aggregation across spatial and temporal dimensions for fine-grained domain alignment, which is extendable to image scenarios. Extensive experiments show that QuadMix outperforms the state-of-the-art works by large margins on four challenging UDA-SS benchmarks.

Several research have been adopted to designrobustorstochasticmodel predictive control (MPC) strategies to tackle the model uncertainty. However, the worst-case events do not occur frequently, and the statistical information is neglected, which may render robust MPC strategies conservative. In addition, the stochastic MPC controller design, which computes the probability of constraint satisfaction or performance improvement using expected indicators, cannot rule out the incident of less probability to reduce the possibly catastrophic consequences. For instance, severe variations in wind speed can affect the airflow into the engine, impacting the compression and combustion processes, which may cause a surge or choke phenomenon. In this paper, we take risk-aware strategies into account for MPC design to tackle the tails of a probability distribution that can contribute to moderating the risks. Utilizing the proposed risk measurement method, we establish a risk-constrained MPC framework to seek tradeoffs between improving the control performance of interest and restricting the risk of catastrophic incidents, and we reformulate the proposed risk-constrained MPC in a favorable form that is computationally tractable. Moreover, to ensure the safety requirement of the turbofan engine operating processes, we explicitly analyze the closed-loop properties, e.g., recursive feasibility and stability, of the proposed risk-constrained MPC method. Lastly, we demonstrate the efficiency of the proposed risk-constrained MPC algorithm on the safety-critical control of turbofan engines using real-world data to verify the availability of the proposed algorithm.Note to Practitioners—Model predictive control (MPC) is fundamental for several applications in the process industry, transportation, and robotics. However, due to the existence of the model uncertainty, there may occur severe constraint violations or even instability for certain safety-critical systems, which regard normally given safety constraints as a priority in the deployment of control algorithm. For instance, in the safety-critical control of turbofan engine, the latent force of the dynamical system can be affected by some external surrounding factors, such as inlet wind speed, ambient temperature, barometric pressure, inlet conditions, etc., which may cause surge or choke phenomenon. To tackle the model uncertainty, several pieces of research have been adopted to design MPC strategies in the presence of uncertainties. Nonetheless, the worst-case events do not occur frequently, and the statistical information is neglected, which rendersrobustMPC strategies extremely conservative. In addition, thestochasticMPC controller design, which computes the probability of constraint satisfaction or performance improvement using expected indicators, does not rule out the incident of less probability to reduce the possibly catastrophic consequences. In view of the threat of the safety-critical control of turbofan engines, a risk measurement method and a risk-constrained model predictive control strategy are proposed. Meanwhile, to guarantee the safety and reliability of turbofan engines, we explicitly analyze the proposed algorithm’s closed-loop properties.

Targeting to improve the time synchronization accuracy of multi-hop Time-Sensitive Wireless Sensor Networks (TS-WSNs) for mission-critical industrial automation applications, a Minimum Variance Time Synchronization (MVTS) algorithm utilizing the concept of Packet-Coupled Oscillators (PkCOs) is proposed. This MVTS algorithm utlizes an output feedback approach to mitigate the impact of communication noise on the accumulation of synchronization errors. In addition, a Time-Division Multiple Access (TDMA) packet-exchange superframe is introduced to achieve efficient and low-overhead time synchronization. The optimal gain matrix of the MVTS algorithm is obtained by the Linear Matrix Inequality (LMI) optimization with theoretic analysis. The proposed MVTS algorithm is evaluated by both simulation and experiments on an IEEE 802.15.4 hardware testbed. The experimental results show that the proposed algorithm can effectively reduce the growth rate of clock offset along multi-hop nodes and improve the time synchronization accuracy of the TS-WSNs. Note to Practitioners—This paper explores a method to achieve precise time synchronization in TS-WSNs. The primary challenge being addressed is the accumulation of synchronization errors that occur in multi-hop TS-WSNs, which can compromise the accuracy of time synchronization. To mitigate this challenge while taking communication overhead into account, we propose a solution that combines a TDMA-based packet-exchange superframe structure with the MVTS algorithm. This approach introduces an output feedback consensus control scheme to minimize synchronization error variance. The optimal gain matrix for this consensus control scheme is derived through LMI optimization. The algorithm is implemented on an IEEE 802.15.4-compatable wireless node SAM R21 by Microchip and the experimental results of a 10-hop netowrk shows that the maximum synchronization error is$8.32\mu s$, reduced by 56% and 32%, repsectivly, compared to the baseline method FTSP and the recent PISync.

In this paper, a P-type adaptive predictive control (PAPC) method is presented for a category of unknown multi-input multi-output (MIMO) discrete-time systems with nonaffine nonlinear dynamics. First, the unknown nonlinear model is altered to a linear form containing an unknown pseudo-partial derivative (PPD) matrix utilizing the partial-form dynamic linearization (PFDL). A predictive model is then established by employing the modified projection algorithm, an auto-regressive model, and an output estimation technique. Based on the predictive model, an adaptive learning law that incorporates estimated tracking error information is used to generate the virtual error. Then, a data-driven PFDL-PAPC algorithm is constructed by replacing the actual tracking error in the P-type controller with the virtual one. The bounded convergence properties of the output estimation and tracking error dynamics are theoretically analyzed using the contraction mapping principle. The effectiveness of the PFDL-PAPC method is demonstrated through coupled tanks and actual data-based blast furnace ironmaking experiments. Note to Practitioners—Model-based control strategies are highly dependent on the model of the controlled plant, which makes it challenging to apply them in complicated industrial processes. In this paper, a P-type adaptive predictive control algorithm is presented. It is directly driven by the virtual error generated through the multi-layer prediction mechanism without requiring any modeling procedure. The operators can flexibly adjust the linearization length according to the system’s dynamic complexity. Furthermore, the proposed algorithm can effectively resist the negative influence of input disturbances. The coupled tanks and actual blast furnace ironmaking data-based experiments are provided to verify the effectiveness of the proposed algorithm.

This paper proposes a data-driven model-free inverse reinforcement learning (IRL) algorithm tailored for solving an inverse$H_{\infty } $control problem. In the problem, both an expert and a learner engage in$H_{\infty } $control to reject disturbances and the learner’s objective is to imitate the expert’s behavior by reconstructing the expert’s performance function through IRL techniques. Introducing zero-sum game principles, we first formulate a model-based single-loop IRL policy iteration algorithm that includes three key steps: updating the policy, action, and performance function using a new correction formula and the standard inverse optimal control principles. Building upon the model-based approach, we propose a model-free single-loop off-policy IRL algorithm that eliminates the need for initial stabilizing policies and prior knowledge of the dynamics of expert and learner. Also, we provide rigorous proof of convergence, stability, and Nash optimality to guarantee the effectiveness and reliability of the proposed algorithms. Furthermore, we showcase the efficiency of our algorithm through simulations and experiments, highlighting its advantages compared to the existing methods.Note to Practitioners—Generally, the cost function for optimal tracking or imitation control is manually defined, which is a challenging task and may result in large tracking errors and slow tracking. In such cases, IRL is a powerful tool for reconstructing proper cost functions. Real-world systems, as demonstrated in practical cases, are frequently exposed to external disturbances and come with unknown models. Employing$H_{\infty } $control is an effective strategy to handle disturbances. However, applying model-free IRL to solve the inverse problem of$H_{\infty } $control for imitation remains an underexplored domain. This paper explores model-free inverse$H_{\infty } $control for imitating expert behaviors, specifically addressing the time-consuming nature of the existing IRL studies that employ a two-loop iteration structure. We propose an efficient single-loop IRL algorithm with a new framework to do this. It is data-driven and model-free, eliminating the need to find an initial stabilizing control policy, which is typically challenging. Additionally, it ensures convergence, stability, and optimality with provable guarantees.

This paper is concentrated on the problem of decentralized output-feedback control of interconnected strict-feedback systems with actuator failures. It is focused on the cases where the virtual control coefficients of the plant are unknown; the global boundedness, matching conditions or global Lipschitz conditions of the interconnections are not assumed; the control algorithm is as simple as possible. They render the existing decentralized output-feedback fault-tolerant control designs infeasible. To address the problem, a low-complexity decentralized robust prescribed performance control approach based on a linear state transformation and an input-driven filter is put forward in this paper. It achieves the system outputs to track the corresponding references with the preassigned speed and accuracy. It is also inherently robust against the unknown system dynamics, the actuator failures, and the disturbances, thus without parameter estimation, function approximation, derivative computation, command filtering, fault detection, fault isolation or fault estimation. Finally, a comparative simulation on two inverted pendulums linked by a spring is conducted to demonstrate the developed control design. Note to Practitioners—Many complex systems, such as power systems, aerospace systems, and chemical systems, can be modeled as interconnected systems. Moreover, due to the increasing scale and complexity of engineering systems, actuator failures are becoming more likely to occur during system operation. On the other hand, both the transient and steady-state tracking performance of the systems are required to be preassigned in practical scenarios, e.g., missile interception. Existing approaches to compensate for the actuator failures guarantee only the boundedness of the tracking error under nonparametric uncertainties in the system model. This paper presents a decentralized robust prescribed performance control approach. It is inherently robust to the system nonlinearities, the actuator failures, and the disturbances. It exhibits lower costs in computation, higher efficiency in design, and is more user-friendly in implementation. It achieves trajectory tracking with preassigned rate and accuracy, despite the actuator failures. Extension of the approach to multi-agent systems with actuator failures is an interesting topic for future investigations.

The current control of alumina digestion process (ALDP) relies on infrequent manual assays for caustic ratio, leading to open-loop operation between assays and suboptimal performance. This paper proposes a novel cloud-edge collaboration (CEC) architecture utilizing deep learning for real-time caustic ratio prediction, enabling closed-loop operation at all times. Our method combines slow-sampled assays with fast-sampled process measurements. An autoregressive moving average with extra inputs (ARMAX) model is used for feature extraction, while an improved bidirectional gated recurrent unit (I-BiGRU) that incorporates a multi-head attention (MHA) mechanism bridges data gaps through prediction. A soft-collaboration mechanism ensures smooth model updates at the edge, enabling operators to make informed control decisions. Theoretical analysis guarantees the convergence of prediction errors. Real-world data from a large-scale alumina plant demonstrates the superior performance of our prediction method compared to the baseline methods. Additionally, industrial experiments validate the effectiveness of the CEC architecture in supporting human operators for ALDP closed-loop operation. Note to Practitioners—This paper was motivated by the challenges of achieving closed-loop operation in the alumina digestion process. The concept of cloud-edge collaboration (CEC) architecture with deep learning offers a powerful solution for various industrial scenarios that rely on infrequent manual measurements, where real-time monitoring of key performance indexes is essential. CEC can continuously predict these values, bridging the gaps between slow, accurate assays and frequent, less precise sensor data. This approach allows for closed-loop operation based on real-time predictions, potentially improving production efficiency and yield. The ability to handle data gaps makes our method applicable to processes with inherent measurement challenges. By implementing CEC in various industries, practitioners can transition from open-loop control to data-driven closed-loop systems, optimizing processes and achieving substantial benefits.

This paper is concerned with the problem of fault-tolerant prescribed performance attitude stabilization for space-craft under model uncertainties and actuator failures. In most of the existing control designs for spacecraft described by the unit quaternion, the possible singularity issue of the virtual control coefficient matrix is neglected such that the controllability of the attitude subsystem cannot be warranted throughout. On the other hand, the related works depend on complex algorithms of approximation, estimation or diagnosis to deal with unknown system dynamics. In this paper, a singularity-free low-complexity fault-tolerant prescribed performance control (PPC) strategy is put forward. To exclude the singularity issue, an initialization principle of the performance envelop is devised. On this basis, a static PPC law is developed, without parameter identification, function approximation, disturbance estimation, failure detection, failure isolation, failure estimation. In place of the classical Lyapunov stability theory, a unified performance analysis framework based on proof by contradiction and the barrier Lyapunov function is constructed. It not only turns out attitude stabilization with the preassigned settling time and accuracy whenever the actuator failures happen, but also discloses the intrinsic robustness of the control system versus actuator failures and model uncertainties. A comparative study on a rigid spacecraft is performed to demonstrate the validity and advantage of our approach.

This paper is dealt with the tracking control problem for the multi-input multi-output (MIMO) block-triangular nonlinear systems with output constraints under discontinuous references. It is focused on the case where the system exhibits inherent nonlinearities, e.g., radically unbounded nonlinearities, and totally unknown dynamics. This renders the existing solutions infeasible. To surmount this challenge, a novel hybrid tracking control strategy composed of a robust decoupling constrained controller and a proportional controller is devised in this paper. It guarantees that the system outputs evolve within the prescribed constraint bands and track the discontinuous references with the tunable settling time and accuracy. Moreover, the controller shows a significant simplicity. No attempt is made for parameter identification, function approximation, disturbance estimation, derivative calculation or command filtering, despite the unknown system dynamics and the recursive control design. The theoretical findings are validated by a comparative experiment on a 2-DOF serial flexible link (2DSFL) robot.

Early faults, with their small amplitudes and slow variations, are easily masked by noise or process trends, making detection difficult. Also, observer-based methods struggle with timely and accurate early fault detection. To address these challenges, a novel observer-driven temporal graph convolutional network (OD-TGCN) is proposed for early fault detection of control systems subject to semi-stationary periodic input disturbances and measurement noise. We employ graph representation to describe the mechanistic relationships and capture temporal features between variables in the control system. First, graph nodes are constructed based on the control inputs, output signals of the control systems and the fault detection residual of the disturbance decoupling generalized proportional-integral observer (DD-GPIO). Then, a method for constructing the adjacency matrix of the graph based on the system matrix is provided. Finally, the output of the temporal graph convolutional network (TGCN) is passed to a Multi-Layer Perceptron (MLP) for graph-based fault detection. The proposed method is applied to a two-wheeled self-balancing robot. Comparative results show that OD-TGCN significantly outperforms DD-GPIO and typical TGCN in early fault detection accuracy. Additionally, OD-TGCN exhibits notable robustness across datasets with different disturbances/noise.

Anomaly detection in complex industrial processes plays a pivotal role in ensuring efficient, stable, and secure operation. Existing anomaly detection methods primarily focus on analyzing dominant anomalies using the process variables (such as arc current) or constructing neural networks based on abnormal visual features, while overlooking the intrinsic correlation of cross-modal information. This paper proposes a cross-modal Transformer (dubbed FmFormer), designed to facilitate anomaly detection by exploring the correlation between visual features (video) and process variables (current) in the context of the fused magnesium smelting process. Our approach introduces a novel tokenization paradigm to effectively bridge the substantial dimensionality gap between the 3D video modality and the 1D current modality in a multiscale manner, enabling a hierarchical reconstruction of pixel-level anomaly detection. Subsequently, the FmFormer leverages self-attention to learn internal features within each modality and bidirectional cross-attention to capture correlations across modalities. By decoding the bidirectional correlation features, we obtain the final detection result and even locate the specific anomaly region. To validate the effectiveness of the proposed method, we also present a pioneering cross-modal benchmark of the fused magnesium smelting process, featuring synchronously acquired video and current data for over 2.2 million samples. Leveraging cross-modal learning, the proposed FmFormer achieves state-of-the-art performance in detecting anomalies, particularly under extreme interferences such as current fluctuations and visual occlusion caused by heavy water mist. The presented methodology and benchmark may be applicable to other industrial applications with some amendments. The benchmark will be released athttps://github.com/GaochangWu/FMF-Benchmark.

This article is concerned with the high-performance leader-following problem for the heterogeneous nonlinear multiagent systems. It is focused on the cases where the underlying communication graph is directed and switching; the model information of each agent is unknown; only the relative output measurement is available for the local controller design. They render the existing distributed high-performance control solutions infeasible. In this article, a distributed robust output-feedback prescribed performance control strategy is put forward to conquer this obstacle. First, the resulting control is off-line designed, regardless of the initial condition or the switching condition. Second, it automatically adjusts the neighborhood errors and the intermediate errors online, against the topology switching. Besides, it is inherently robust to the unknown system dynamics, without parameter identification, function approximation, disturbance estimation, or derivative calculation or estimation. It turns out that global fast accurate output synchronization is achieved by our approach in the sense that the follower outputs track the leader output with the preassigned settling time and accuracy after the topology switching. A comparative simulation is conducted to substantiate the above theoretical findings.

Surrogate-assisted evolutionary algorithms (SAEAs) rely on the infill criterion to select candidate solutions for expensive evaluations. However, in the context of expensive constrained multi-objective optimization problems (ECMOPs) with complex feasible regions, guiding the optimization algorithm towards the constrained Pareto optimal front and achieving a balance between feasibility, convergence, diversity, exploration, and exploitation using a single infill criterion pose significant challenges. We propose an ensemble infill criterion-based multi-stage SAEA (EIC-MSSAEA) to tackle these challenges. Specifically, EIC-MSSAEA comprises three stages. In the first stage, we ignore constraints to facilitate the rapid traversal of infeasible obstacles. In the second stage, only one constraint is activated at a time to increase algorithm diversity. Finally, in the last stage, we activate all constraints to improve overall feasibility. In each stage, EIC-MSSAEA first employs NSGA-III as the underlying baseline solver to explore the search space, in which promising solutions are then selected by an ensemble infill criterion that incorporates multiple base-infill criteria to measure the feasibility, convergence, diversity, and uncertainty of candidate solutions. Experimental results demonstrate the competitiveness of EIC-MSSAEA against state-of-the-art SAEAs for ECMOPs.

The problem of high-performance tracking control for the nonlinear systems with nonparametric uncertainties as well as time-varying disturbances is investigated in this paper. In place of the methods of sliding-mode control, variable structure control, and robust integral of the sign of the error control, an output- feedback fuzzy prescribed performance control approach is put forward. It consists of a fuzzy state observer, a proportional- integral (PI) constraint-handling scheme, and an adaptive fuzzy backstepping PI control unit. The developed approach achieves output tracking with the predefined settling time and accuracy. Moreover, it actively restrains the oscillations of both the tracking error and the intermediate errors. On the other hand, the compact set condition for fuzzy approximation is rigorously warranted. Besides, the chattering phenomenon and the requirement for differentiable disturbances by the existing methods are excluded. A pair of comparative simulations is performed to demonstrate the efficacy and advantage of our approach.

Grid modernization and large-scale integration of inverter-based resources (IBRs) into distribution systems have resulted in the development of new control strategies relying on information and communication technologies. To this end, this article proposes a distributed secondary control algorithm using an event-triggered mechanism for exchanging information among IBRs over digital communication channels in islanded modern microgrids. Unlike the existing event-triggered studies, the proposed method is based on a nonlinear mapping technique for encoding shared data over digital communication channels, making it suitable for real-world applications. It enables the control system to use digitized and encoded data instead of typical continuous analog information, resulting in the more efficient usage of communication infrastructures. As a result, it can be regarded as a practical algorithm for stabilizing voltage and frequency during the transient and steady-state response of autonomous modern microgrids considering computational constraints and the limited bandwidth of communication systems. Finally, comparative simulation studies and experimental results validate the performance and effectiveness of the proposed algorithm.

Precise online prediction of caustic concentration is crucial for optimal control and operating efficiency in alumina production. However, existing supervised learning models rely heavily on a large amounts of labeled assay samples, which are time-consuming and resource intensive to obtain. To overcome this challenge, we propose a novel semisupervised learning approach that effectively uses both labeled and unlabeled data. Our semisupervised approach, based on a transformer variational autoencoder (TVAE), incorporates a multihead decoder for joint reconstruction and prediction tasks. Through robust regularizing and jointly learning these tasks, the TVAE constructs a smooth, low-dimensional, and continuous latent representation that captures hidden patterns and relationships within the data, enabling accurate predictions with significantly fewer labeled samples compared to existing methods. Experiments on a real-world alumina production process demonstrate the superior performance of our approach in predicting caustic concentration, outperforming state-of-the-art semisupervised time-series prediction models.

Deep learning has become increasingly important in fault diagnosis, but it relies on a large amount of high-quality labeled data. Collecting data from distributed machines can expand the dataset, but it usually leads to privacy concerns. Moreover, since the operating conditions are complex in real-world applications, the collected training data and the test data often have different distributions. Therefore, a well-trained model on the training data may not be suitable for test data due to the domain shift. To preserve privacy and to mitigate the domain shift, in existing federated transfer learning fault diagnosis methods, distributed machines exchange model parameters and features rather than raw data with the central server. However, such methods suffer from a single point of failure and high communication burden. To address these issues, we propose a fully decentralized federated transfer learning fault diagnosis method. More specifically, the proposed method obtains a pretrained model among source nodes with labeled training data where each source node exchanges model parameters with its neighboring source nodes. Moreover, a novel transfer learning strategy is proposed, which aligns features of test data at the target node with features of training data at its connected source nodes to mitigate misclassifications resulting from the domain shift. The effectiveness of the proposed method is verified by various experiments on two public bearing datasets.

Deep learning offers promising solutions for quality prediction in industrial processes, improving decision-making and performance monitoring. In this article, we propose a novel deep learning framework that incorporates variable time delay (VTD) estimation and uncertainty quantification into quality prediction. The framework employs a collaborative method that integrates deep Q-network with random forest to estimate VTD values. It then utilizes a hybrid BMCR model, consisting of parallel bidirectional minimal gated unit and 1-D convolutional layers, along with a residual connection, specifically designed to capture both long-term and short-term features in industrial data. The framework produces prediction intervals directly to quantify the uncertainty in the prediction results. This combined method offers high-precision point predictions alongside uncertainty quantification, providing valuable insights for industrial decision-making. The effectiveness of the proposed method is validated through two numerical examples, a benchmark, and a real-world industrial case from the alumina digestion process.

Consistent product quality and efficient operations in high-pressure grinding roll (HPGR) rely heavily on real-time anomaly detection. Complexities arise from fluctuations in raw materials, feeding processes, and unforeseen disruptions, along with the inherent spatio-temporal dynamics of sensor data. This article addresses these challenges by proposing a collaborative anomaly monitoring architecture that leverages the cloud, edge devices, and a powerful algorithm: The spatio-temporal attention-based (STA) minimal gated unit (MGU) adversarial autoencoder (AAE). The proposed algorithm, trained in the cloud, analyzes sensor data encompassing information, material, and energy flows within the HPGR. Its core strength lies in capturing the intricate interplay between spatial and temporal data patterns through a novel spatio-temporal attention mechanism. In addition, adversarial training enhances the model's ability to distinguish normal operations from anomalies. Edge devices perform real-time monitoring and transmit preprocessed data to the cloud for STA–MGU–AAE analysis. The extracted features not only enable accurate anomaly detection in process variables, but also facilitate root cause analysis, leading to significant improvements in process stability and reliability. The effectiveness of the proposed architecture is validated through practical beneficiation experiments, demonstrating its potential to revolutionize HPGR anomaly monitoring in production processes.

In this paper, we aim to achieve robust and efficient train rescheduling of intercity express railway lines considering periodic train timetables and passenger uncertainties. Considering intercity express railways’ features of longer section lengths, multiple trains running in close succession within long sections and the varying passenger demands, we improve the multi-train state space model of intercity express railway operation, in which the changes and uncertainties of passenger flows are modeled as system parameter uncertainties and the primary train delays caused by temporary speed restriction extreme weather, and signal failure, etc. are modeled as external interference. Then, a real-time train rescheduling controller is developed that combines iterative learning and model predictive control to enhance its resistance against both the model uncertainties (i.e., varying passenger flows) and the external interference (i.e., the primary delays). The objective function of the rescheduling controller is to recover from delays while preventing the control force amplitude from becoming excessively large. Each period of the periodic timetable is modeled as a batch, and a batch-based state space error predictive model is developed to simultaneously recover the nominal timetable and minimize the control force amplitude. It is proven that the error norm will eventually converge to a bounded value as the number of iterations increases. The performance of the proposed method is evaluated through simulations based on the Beijing–Tianjin intercity express rail line.

Dyadic control is a new control frontier, which is to address two (often conflicting) objectives simultaneously. We consider rendering bothcompact formationandcollision avoidancein uncertain cooperative road transportation systems. These two tasks, however, present conflicting objectives, where excessive stress on formation tightness may lead to an increased risk of collisions. The tasks are creatively formulated as equality constraints and inequality constraints. Based on the generalized Udwadia-Kalaba (GUK) equation, two independent controllers are developed to handle these constraints, with orthogonality between the control components ensuring no mutual interference. The proposed method guarantees the uniform boundedness and uniform ultimate boundedness of the system, even in the presence of unknown uncertainties. The effectiveness of the control strategy is demonstrated through simulations of a four-vehicle fleet system.

In mineral processing, the dynamic nature of industrial data poses challenges for decision-makers in accurately assessing current production statuses. To enhance the decision-making process, it is crucial to predict comprehensive production indices (CPIs), which are influenced by both human operators and industrial processes, and demonstrate a strong dual-scale property. To improve the accuracy of CPIs' prediction, we introduce the high-frequency (HF) unit and low-frequency (LF) unit within our proposed dual-scale deep learning (DL) network. This architecture enables the exploration of nonlinear dynamic mapping in dual-scale industrial data. By integrating the Cloud-Edge collaboration mechanism with DL, our training strategy mitigates the dominance of HF data and guides networks to prioritize different frequency information. Through self-tuning training via Cloud-Edge collaboration, the optimal model structure and parameters on the cloud server are adjusted, with the edge model self-updating accordingly. Validated through online industrial experiments, our method significantly enhances CPIs' prediction accuracy compared to the baseline approaches.

This article is focused on the problem of prescribed performance control (PPC) for the strict-feedback systems under actuator failures with dynamic redundancies and deferred actuator replacement. It is concentrated on the cases where both the multiplicative nonlinearities and the additive nonlinearities of the plant are unknown and the fault-tolerant control (FTC) algorithm is as simple as possible. They render the existing solutions infeasible. In this article, we develop a low-complexity fault-tolerant PPC (FTPPC) approach, which is made up of a nominal controller, a fault detection module, and a reconfigurable controller. It ensures reference tracking with the predetermined speed and accuracy during the fault-free case and recovers the predefined performance after the deferred actuator replacement. The controller does not rely on the specific knowledge about the system dynamics, the disturbances, or the time profile and bound of the actuator failures. Moreover, it obviates the needs for parameter identification, function approximation, command filtering, and disturbance estimation. A comparative simulation on a jet engine compressor is carried out to demonstrate the above theoretical findings.

This paper presents a novel approach for predicting bi-level human decision-making in mineral processing. This method aims to enhance the adaptability of human decision-making support systems and reduce dependence on expert knowledge. To achieve this, it leverages a combination of evolutionary computation and neural networks. The proposed method utilizes a hierarchical neural network framework optimized through evolutionary computation. This framework takes advantage of the sequential nature of decision-making within the mineral processing system. The effectiveness of the method is validated through experiments using 1,324 real-world data entries from a mineral processing plant in China. The results demonstrate the method’s capability to improve prediction accuracy.

Industrial streaming data exhibits the concept drift characteristic due to the time-variant operating conditions, which degrades the performance of models established by traditional offline batch learning. Moreover, the widespread outliers and the correlations between data variables in industrial data streams can have a devastating impact on modeling. Therefore, this paper presents a novel online sequential sparse robust neural networks with random weights (OSSR-NNRW) for imperfect industrial streaming data to achieve highly reliable online modeling of time-variant dynamic systems. First, sparse partial least squares regression is used to replace least squares estimation for network output weights calculation, which not only can effectively solve the multicollinearity problem caused by correlations, but also enable variable selection to improve the performance and interpretability. Second, we introduce the online sequential learning strategy with forgetting factor to realize adaptive updating of model parameters, thus enhancing the online learning ability and overcoming the time-variant dynamics of industrial systems. More importantly, in order to strengthen the robustness of the model, Schweppe generalized M-estimation is adopted to determine the modeling weights by the model residual size and the distance information of input vectors in the high-dimensional space to resolve the prevalent existence of outliers in the input and output samples. Finally, data experiments on two industrial systems have validated the effectiveness, advancement, and practicality of the proposed method. Note to Practitioners—In the process industry, the product quality relies on the timely and accurate measurement of key production indicators. However, owing to the time-varying characteristics of industrial processes and the limitations of measurement devices, conventional batch learning-based data-driven models are difficult to apply to imperfect industrial data stream scenarios. To this end, the OSSR-NNRW is proposed for online robust modeling of complex time-variant dynamic systems by combining sparse robust modeling and online learning strategy in a unified framework of neural networks with random weights. The OSSR-NNRW enables online learning based on industrial data streams while resolving correlations between data variables and mitigating the negative effects of outliers from both input and output samples on the modeling process. Experimental results using two typical process industry datasets show that the proposed OSSR-NNRW has high estimation accuracy and can be easy to implement in industrial processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

Bilevel optimization is a special type of optimization in which one problem is embedded within another. The bilevel optimization problem (BLOP) of which both levels are multiobjective functions is usually called the multiobjective BLOP (MBLOP). The expensive computation and nested features make it challenging to solve. Most existing studies look for complete lower-level solutions for every upper-level variable. However, not every lower-level solution will participate in the bilevel Pareto-optimal front. Under a limited computational budget, instead of wasting resources to find complete lower-level solutions that may not be in the feasible region or inducible region of the MBLOP, it is better to concentrate on finding the solutions with better performance. Bearing these considerations in mind, we propose a multiobjective bilevel optimization solving routine combined with a knee point driven algorithm. Specifically, the proposed algorithm aims to quickly find feasible solutions considering the lower-level constraints in the first stage and then concentrates the computational resources on finding solutions with better performance. Besides, we develop several multiobjective bilevel test problems with different properties, such as scalable, deceptive, convexity, and (dis)continuous. Finally, the performance of the algorithm is validated on a practical petroleum refining bilevel problem, which involves a multiobjective environmental regulation problem and a petroleum refining operational problem. Comprehensive experiments fully demonstrate the effectiveness of our presented algorithm in solving MBLOPs.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

In this article, we develop two invariance principles for nonlinear discrete-time switched systems based on multiple Lyapunov functions and multiple weak Lyapunov functions, respectively, which allow the first differences of multiple weak Lyapunov functions to be positive on certain sets. It is shown that the solution of the system is attracted to the largest weakly invariant set in a certain specific region. Then, based on the invariance principle developed and geometrical dissipativity, we obtain the generalized output synchronization for discrete-time dynamical networks with nonidentical nodes by an appropriate switching among several communication topologies. Finally, two examples are provided to demonstrate the effectiveness of the main results.

This article dedicates to investigating a methodology for enhancing adaptability to environmental changes of reinforcement learning (RL) techniques with data efficiency, by which a joint control protocol is learned using only data for multiagent systems (MASs). Thus, all followers are able to synchronize themselves with the leader and minimize their individual performance. To this end, an optimal synchronization problem of heterogeneous MASs is first formulated, and then an arbitration RL mechanism is developed for well addressing key challenges faced by the current RL techniques, that is, insufficient data and environmental changes. In the developed mechanism, an improved Q-function with an arbitration factor is designed for accommodating the fact that control protocols tend to be made by historic experiences and instinctive decision-making, such that the degree of control over agents' behaviors can be adaptively allocated by on-policy and off-policy RL techniques for the optimal multiagent synchronization problem. Finally, an arbitration RL algorithm with critic-only neural networks is proposed, and theoretical analysis and proofs of synchronization and performance optimality are provided. Simulation results verify the effectiveness of the proposed method.

This article studies the containment control problem of nonlinear multiagent systems (MASs) subjected to communication link faults and dead-zone inputs. In case of an unknown fault in the communication link, there is no constant Laplacian matrix anymore and each follower agent cannot be informed of the global information simultaneously. To deal with this problem, an adaptive compensating estimator is constructed to estimate the signal spanned by the leaders. Instead of using the linear filter, a nonlinear filter is employed, which both solves the classical complexity explosion in the traditional backstepping method and flushes out the usefulness of the boundary layer error. Considering the dead zone input, we propose two event-triggered schemes, that is, the update-triggered scheme and the transmit-triggered scheme. In the former, the threshold function involves the tracking errors and additional dynamic variable, which can provide the desirable tradeoff between the containment control performance of the considered MASs and saving communication resources. In the latter, the triggered condition is designed according to the characteristic of dead zone, which makes the communication burden be reduced further. Following the backstepping design framework, an adaptive containment control is constructed, it is shown that the containment error can converge to an adjustable residual set even if MASs are subjected to the unknown and bounded communication link faults and dead-zone inputs. Finally, an example is given to show the effectiveness of the proposed results.

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable. To overcome these limitations, we propose a quantized distributed zeroth-order algorithm, which integrates the deterministic gradient estimator, the standard uniform quantizer, and the distributed gradient tracking algorithm. We establish linear convergence to a global optimal point for the proposed algorithm by assuming Polyak-Łojasiewicz condition for the global cost function and smoothness condition for the local cost functions. Moreover, the proposed algorithm maintains linear convergence at low-data rates with a proper selection of algorithm parameters. Numerical simulations validate the theoretical results.

This article studies the trajectory imitation control problem of linear systems suffering external disturbances and develops a data-driven static output feedback (OPFB) control-based inverse reinforcement learning (RL) approach. An Expert-Learner structure is considered where the learner aims to imitate expert's trajectory. Using only measured expert's and learner's own input and output data, the learner computes the policy of the expert by reconstructing its unknown value function weights and thus, imitates its optimally operating trajectory. Three static OPFB inverse RL algorithms are proposed. The first algorithm is a model-based scheme and serves as basis. The second algorithm is a data-driven method using input-state data. The third algorithm is a data-driven method using only input-output data. The stability, convergence, optimality, and robustness are well analyzed. Finally, simulation experiments are conducted to verify the proposed algorithms.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine-cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

Electric-powered wheelchairs play a vital role in ensuring accessibility for individuals with mobility impairments. The design of controllers for tracking tasks must prioritize the safety of wheelchair operation across various scenarios and for a diverse range of users. In this study, we propose a safety-oriented speed tracking control algorithm for wheelchair systems that accounts for external disturbances and uncertain parameters at the dynamic level. We employ a set-membership approach to estimate uncertain parameters online in deterministic sets. Additionally, we present a model predictive control scheme with real-time adaptation of the system model and controller parameters to ensure safety-related constraint satisfaction during the tracking process. This proposed controller effectively guides the wheelchair speed toward the desired reference while maintaining safety constraints. In cases where the reference is inadmissible and violates constraints, the controller can navigate the system to the vicinity of the nearest admissible reference. The efficiency of the proposed control scheme is demonstrated through high-fidelity speed tracking results from two tasks involving both admissible and inadmissible references.

This article is concerned with the problem of reference tracking for the lower-triangular nonlinear systems with a chain of odd powers. Contrary to most of the related studies, this work is focused on the case where neither the odd powers nor their bounds are known. This renders the majority of the existing methods for stability analysis and control design for the odd-power systems infeasible. To surmount this challenge, a robust prescribed performance control strategy together with a constraint analysis by contradiction is put forward. Instead of the well-established adding one power integrator technique, a group of barrier functions are employed to combat the tracking error and the intermediate errors. In lieu of the Lyapunov stability theory, a constraint analysis by contradiction is carried out, which discloses the inherent robustness of the control system against the nonparametric uncertainties, the unmatched disturbances and the unknown odd powers. It is guaranteed that the tracking error enters into a preassigned neighborhood of zero after a given time, with a predefined bound on the overshoot. In addition, the proposed control exhibits a striking simplicity. Despite the severe model uncertainties and the recursive control design, no effort needs to be paid for parameter identification, function approximation, disturbance estimation, or derivative calculation. The above theoretical findings are substantiated by the comparative simulation results.

Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagrams are available in many scientific platforms and industrial applications, there is still a lack of customized research in production performance monitoring. Therefore, this article proposes an interactive visual analytics approach for monitoring the heavy-plate production process (iHPPPVis). Specifically, a multicategory aggregated monitoring framework is proposed to facilitate production performance monitoring under varying working conditions. In addition, A set of visualizations and interactions are designed to enhance analysts' analysis, identification, and perception of the abnormal production performance in heavy-plate production data. Ultimately, the efficacy and practicality of iHPPPVis are demonstrated through multiple evaluations.

Aiming at the operation optimization of the wastewater treatment process (WWTP) with nonstationary time-varying dynamics and complex multiconstraint, this article proposes a novel adaptive constraint penalty decomposed multiobjective evolutionary algorithm with synthetical distance (SD)-based cross-generation crossover. First, the concept of spatial SD is presented to comprehensively evaluate the similarity of individual solutions from two aspects of distance and angle, and the individual information between two adjacent generations is used to enhance the diversity of individuals and accelerate the convergence of the algorithm. Second, aiming at the complex multiconstraint during the operation optimization of WWTP, an adaptive penalty algorithm is further adopted to punish the individual solutions that violate the constraints, so as to improve the handling efficiency and success rate of constraints. Furthermore, in view of the time-varying dynamics of actual WWTP, a recursive bilinear subspace identification method based on sliding window is adopted to establish the optimization models as well as the constraint models with self-learning parameter, which provides accurate model guarantee for high-performance multiobjective operation optimization. Finally, the effectiveness, superiority, and practicability of the proposed method are verified through test function experiments as well as operation optimization control experiments of WWTP.

Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multi-population evolution based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.

Evolutionary multitasking Optimization (EMTO) is a paradigm that optimizes multiple tasks simultaneously to improve the overall performance of all tasks by seamlessly transferring useful knowledge among them. Although EMTO has received significant interest, rare studies consider handling tasks that are multimodal optimization problems (MMOPs) with multiple global optimal solutions. Due to the multiple different modalities of each task, a major challenge of solving multiple MMOPs is how to extract and transfer knowledge across modalities of different tasks. To this end, this paper designs a distributed knowledge transfer based evolutionary multitask multimodal optimization (EMTMO-DKT) approach for solving multiple MMOPs simultaneously by discovering and utilizing local knowledge across modalities of different tasks. Specifically, we first divide the population of each task into multiple subpopulations, where each subpopulation explores a modality. Then, we propose an evolution path based similarity measurement to measure the local similarities between subpopulations of different tasks. Since the modalities can be locally similar across tasks, we develop a subpopulation cross matching strategy according to the obtained similarities to pair subpopulations of different tasks. In this stage, the successfully paired subpopulations are allowed to transfer knowledge. Finally, the knowledge transfer probability self-adjusting strategy is applied to each subpopulation to balance knowledge transfer and self-evolution, so as to improve search efficiency. In this paper, a set of multitask multimodal optimization test problems are constructed to assess the efficacy of compared algorithms. Experimental results on both the benchmark functions and the real-world optimization problem demonstrate that the proposed algorithm can quickly locate more global optima in comparison with state-of-the-art EMTO and multimodal optimization algorithms.

The slurry concentration is a crucial factor in mineral processing production, affected by both upstream and downstream systems, including closed-loop control systems. The variability in slurry concentration presents a challenge due to its complex, nonlinear nature and the difficulty in accurately modeling this dynamic system for real-time monitoring. This article introduces an innovative approach for the real-time prediction of slurry concentration. Our method comprises two key components: a mechanistic model and a nonlinear dynamic system. The mechanism model is developed using the closed-loop input error method, effectively mitigating the influence of control systems. We have rigorously demonstrated the convergence of parameters and the stability of the identification process. Further, a hybrid deep neural network, called convolutional neural network-gated recurrent unit (GRU), is proposed to tackle challenges, such as intervariable dependencies and the inherent nonlinear dynamics of the process. The network integrates an autoregressive integrated moving average (ARIMA) model for laboratory delays. A parallel GRU network captures nonlinear dynamic characteristics after the ARIMA model. This architecture ensures real-time online concentration forecasting. The effectiveness of our approach has been substantiated with actual production data from a large-scale mineral processing facility.

This article investigates the detection and identification of physical faults in devices and false-data-injection attacks in communication networks for multi-agent systems with event-triggered transmission mechanisms and subject to external periodic disturbances. First, a new detection and identification scheme, including a local disturbance decoupling (LDD) observer and a distributed disturbance decoupling (DDD) observer, is proposed. Then, based on zero-assignment and the rank-deficiency of the transfer function matrix at zeros, a co-design method for the LDD observer and DDD observer is proposed, which enables the decoupling of periodic disturbances from the residuals for detection and identification. This new scheme no longer requires the transmission of control signals from the node being monitored or the exchange of information between its neighbors, significantly reducing the communication overhead and enhancing the system's security. Finally, a simulation based on a multi-two-wheeled trolley system is used to verify the effectiveness of the proposed method.

Modern industrial process modeling requires models to adapt quickly to real-time operating conditions. To this end, this article proposes a fast sensitivity analysis (SA)-based self-organizing broad learning system (SASO-BLS) that offers a paradigm for theory-guided online structural self-adaptation of differentiable models. Specifically, SASO-BLS is implemented by embedding in BLS an interpretable and efficient model compression method called fast partial differential-based SA (FPD-SA). Unlike conventional SA methods that require iterative evaluation of SA indexes for all samples, FPD-SA exploits the deduced chain rule across categories, effectively mitigating the computational burden imposed by industrial Big Data and reducing computational time. In addition, we derive the offline SASO-BLS algorithm for discrete data and extend it to the online scenario for real-time streaming data processing. Note that both modes obviate the necessity of recalculating the pseudo-inverse of the entire state matrix, facilitating SASO-BLS in attaining remarkable efficiency in structural self-organization. Finally, a theoretical justification of the universal approximation property for SASO-BLS is presented. Experimental results on a fault diagnosis benchmark dataset and a real industrial process one demonstrate the effectiveness of the proposed approach.

In intercity high-speed railways (HSR) with high speeds and dense traffic, fast decision-making in timetable rescheduling is critical to minimize delays and maintain regular services during disruptions. Different from traditional mathematical programming methods, which are often too computation-intensive for real-time implementation, this paper develops a state-space dynamic model of train traffic with the extension to accommodate multiple trains in the sections between stations. Variations of the state space model are established for scenarios of mild delays and severe delays, respectively. Two automatic rescheduling state feedback controllers are designed to achieve two objectives, to restore the nominal timetable in case of mild delays and to retain regular departure intervals in case of severe delays, respectively. Stability analysis theoretically proves the stability and convergence of the proposed feedback controller and real-time rescheduling algorithm. The proposed rescheduling method indeed is a real-time state feedback controller, and the simulation results of the Beijing-Tianjin intercity HSR show that the proposed method features negligible computation times in the order of microseconds, in contrast to the 56s and 65s required by conventional Mixed-Integer Programming (MIP) for nominal timetable recovery and regular departure interval problems, respectively. The proposed state-space feedback control rescheduling method is quasi-optimal compared to MIP with the added advantage of greater computational efficiency and fast decision-making.

Agile formation (AF) is a new frontier for intelligent swarm system formation. The AF pertains to perform various tasks in short phases of work and frequent reassessment and adaptation of plans. This greatly increases the applicability of swarm systems. There are however two major challenges for the control design: smooth task transitions and guaranteed collision avoidance. We adopt the constraint-following approach to address these. First, for agile formation, a plateau activation function is proposed to generate a sequence of consecutive and disjoint formations. For collision avoidance, a distance-gauge function is proposed. Second, by taking the objectives of agile formation control and collision avoidance as desirable constraints, the agile formation together with collision avoidance are both cast into a constraint following control problem. Third, to evaluate the constraint-following error, a performance measure$\beta $is introduced and then an agile formation control is designed to render the$\beta $-measure to be asymptotically convergent to zero. By this, the swarm system can follow the agile formation constraint and collision avoidance constraint. Therefore, agile formation and collision avoidance are both accomplished.

This paper is concerned with the problem of fault-tolerant dynamic positioning (DP) for the marine surface vessels with sensor faults and unknown dynamics as well as random disturbances. It is anticipated that the requisite performance for the faulty system still holds, especially for the post-fault phrase, which remains open in the literature. In this paper, a fault-tolerant prescribed performance control approach is put forward to solve the problem. It achieves DP with the prescribed speed and accuracy in the sense that the DP errors evolve within the preselected performance envelops whenever the sensor faults happen. It is also inherently robust against the unknown vessel dynamics and ocean disturbances. Thus, the common assumptions on the partially known vessel nonlinearities and the differentiable disturbances are eliminated. On the other hand, there is no need for disturbance estimation, parameter identification, function approximation, fault detection, fault isolation or fault estimation, yielding a control simplicity. Finally, a pair of comparative simulations on Cybership II are carried out to validate the feasibility and advantage of the proposed approach.

In this article, a model-free Q-learning algorithm is proposed to solve the tracking problem of linear discrete-time systems with completely unknown system dynamics. To eliminate tracking errors, a performance index of the Q-learning approach is formulated, which can transform the tracking problem into a regulation one. Compared with the existing adaptive dynamic programming (ADP) methods and Q-learning approaches, the proposed performance index adds a product term composed of a gain matrix and the reference tracking trajectory to the control input quadratic form. In addition, without requiring any prior knowledge of the dynamics of the original controlled system and command generator, the control policy obtained by the proposed approach can be deduced by an iterative technique relying on the online information of the system state, the control input, and the reference tracking trajectory. In each iteration of the proposed method, the desired control input can be updated by the iterative criteria derived from a precondition of the controlled system and the reference tracking trajectory, which ensures that the obtained control policy can eliminate tracking errors in theory. Moreover, to effectively use less data to obtain the optimal control policy, the off-policy approach is introduced into the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by a numerical simulation.

This article proposes a data-driven inverse reinforcement learning (RL) control algorithm for nonzero-sum multiplayer games in linear continuous-time differential dynamical systems. The inverse RL problem in the games is solved by a learner reconstructing the unknown expert players' cost functions from demonstrated expert's optimal state and control input trajectories. The learner, thus, obtains the same control feedback gains and trajectories as the expert, only using data along system trajectories without knowing system dynamics. This article first proposes a model-based inverse RL policy iteration framework that has: 1) policy evaluation step for reconstructing cost matrices using Lyapunov functions; 2) state-reward weight improvement step using inverse optimal control (IOC); and 3) policy improvement step using optimal control. Based on the model-based policy iteration algorithm, this article further develops an online data-driven off-policy inverse RL algorithm without knowing any knowledge of system dynamics or expert control gains. Rigorous convergence and stability analysis of the algorithms are provided. It shows that the off-policy inverse RL algorithm guarantees unbiased solutions while probing noises are added to satisfy the persistence of excitation (PE) condition. Finally, two different simulation examples validate the effectiveness of the proposed algorithms.

During the fused magnesia production process (FMPP), there is a demand peak phenomenon that the demand rises first and then falls. Once the demand exceeds its limit value, the power will be cut off. To avoid mistaken power off caused by demand peak, demand peak needs to be forecast, so multistep demand forecasting is required. In this article, we develop a dynamic model of demand based on the closed-loop control system of smelting current in the FMPP. Using the model prediction method, we develop a multistep demand forecasting model consisting of a linear model and an unknown nonlinear dynamic system. Combining system identification with adaptive deep learning, an intelligent forecasting method for furnace group demand peak based on end-edge-cloud collaboration is proposed. It is verified that the proposed forecasting method can accurately forecast demand peak by utilizing industrial big data and end-edge-cloud collaboration technology.

This article is concerned with the fast and accurate trajectory tracking control problem for a sort of underactuated surface vehicle under model uncertainties and environmental disturbances. A novel neural networks (NNs)-based prescribed performance control strategy is proposed to solve the problem. In the control design, a new type of performance function is constructed which provides a way to predefine the settling time and accuracy, straightforward. Then, a pair of barrier functions are employed to combat not only the position error but also the virtual control input. This evades the possible singularity or discontinuity of the control solution. Next, an initialization technique is exploited, removing the requirement for the initial condition of the control system. Finally, two NNs are employed to deal with the unknown ship nonlinearities. The performance analysis not only demonstrates the effectiveness of the proposed approach but also reveals its robustness against disturbances and unknown reference trajectory derivatives. There is, thus, no need to acquire such knowledge or employ specialized tools to handle disturbances. The theoretical findings are illustrated by a simulation study.

rgb0.00,0.00,0 Turbofan engine performance optimization is usually formulated as a single objective, closed-form optimization problem by employing a prior mechanism model with an additive, user-preference weight. However, in practical scenarios, the conventional single objective performance optimization may not satisfy the high-performance requirements. For instance, pursuing high-effective thrust will lead to high-turbine inlet temperature due to generating extra heat. Moreover, the system model may be inaccurate or even unavailable, mainly due to the degradation factor, manufacturing tolerance, or time-intensive experiments. Traditionally, the multiobjective optimization methods may require a certain amount of function evaluations, or the convergence properties may not be guaranteed explicitly. To tackle the above-mentioned issues, we formulate the performance optimization of turbofan engines as a multiobjective optimization problem and construct a gradient-free framework to deal with the issue of an inaccurate/unavailable turbofan engine model. Then, to ensure the safety requirement of the turbofan engine operating processes, a multiobjective optimization algorithm is proposed utilizing a gradient-free method, termed Hessian aware gradient estimation-based randomized search (HAGE-RS), and we analyze the corresponding convergence properties of the solved candidates. Finally, we illustrate the proposed algorithm on benchmarks and the performance optimization problem using real-world turbofan engine data under different operating conditions to show superior performance.

This article is concerned with the problem of tracking control with discontinuous references for the strict-feedback systems with both multiplicative and additive nonlinearities as well as unmatched disturbances. In contrast with the existing studies, it is focused on the cases where the system nonlinearities are radially unbounded; the system dynamics or its bounding functions are unknown; and the reference derivatives are unavailable. They significantly challenge the existing control solutions under discontinuous references which are based on filtering, guidance, or impulsive systems. To conquer this obstruction, a novel hybrid control scheme is devised in this article, which consists of a robust constraint-handling controller and a proportional controller. It steers the system output to track the discontinuous reference with tunable setting time and accuracy, without violation of the prescribed constraint. Moreover, the controller exhibits a significant simplicity. While it is independent of the specific model information of the plant or the derivatives of the intermediate control signals, no effort is paid for parameter identification, function approximation, command filtering, or disturbance estimation. Finally, three simulation studies are conducted to substantiate the theoretical result.

Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://github.com/Peacefulyang/DSE_MFS.git.

With the increasing penetration of renewable energy and flexible loads in smart grids, a more complicated power system with high uncertainty is gradually formed, which brings about great challenges to smart grid operations. Traditional optimization methods usually require accurate mathematical models and parameters and cannot deal well with the growing complexity and uncertainty. Fortunately, the widespread popularity of advanced meters makes it possible for smart grid to collect massive data, which offers opportunities for data-driven artificial intelligence methods to address the optimal operation and control issues. Therein, deep reinforcement learning (DRL) has attracted extensive attention for its excellent performance in operation problems with high uncertainty. To this end, this article presents a comprehensive literature survey on DRL and its applications in smart grid operations. First, a detailed overview of DRL, from fundamental concepts to advanced models, is conducted in this article. Afterward, we review various DRL techniques as well as their extensions developed to cope with emerging issues in the smart grid, including optimal dispatch, operational control, electricity market, and other emerging areas. In addition, an application-oriented survey of DRL in smart grid is presented to identify difficulties for future research. Finally, essential challenges, potential solutions, and future research directions concerning the DRL applications in smart grid are also discussed.

This paper investigates distributed control protocols design for uncertain nonlinear multi-agent systems with the goal of achieving the optimal consensus. The critical challenges encountered when designing the optimal distributed control protocols are mainly caused by the internal coupling of agents, uncertainty and nonlinear dynamics. Communication delay among agents makes overcoming these challenges even more difficult. To this end, a novel sliding mode control design method is developed based on the sliding mode control principle and the reinforcement learning technique. The remarkable highlights of the developed method in this paper include the design of distributed sliding mode controllers and the integrated framework of sliding mode control and reinforcement learning, which bring the outcome of successfully learning the composite distributed control protocols for multi-agent systems. Thus, all agents can successfully eliminate the negative impacts brought by system uncertainties and communication delay among agents, and finally follow the leader with a nearly optimal approach. The reachability of sliding mode surfaces and the optimal consensus are rigorously proven and analyzed. Finally, simulation results illustrate the effectiveness of the developed method.

In this article, the event-triggered output regulation problem (EORP) under the denial-of-service (DoS) attacks is considered for networked switched systems (NSSs) with unstable switching dynamics (USDs). The USDs here refer to the unsolvable output regulation of each subsystem and the destabilization at partial switching instants, which indicates that the Lyapunov function does not decrease monotonically in activation intervals of each subsystem and increases at partial switching instants. First, long-duration DoS attacks (LDDAs) are considered, where LDDAs imply that their duration may be longer than the total dwell time (DT) of several adjacent activated subsystems. By imposing constraints at switching instants, consecutive asynchronous subsystem switching caused by LDDAs and USDs is allowed, that is, the subsystem switches several times but the controller switching is blocked by LDDAs and controllers fail to switch correspondingly. Second, mixed event-triggered mechanisms (ETMs), combining event-triggered conditions and periodic sampling conditions, are designed to reduce network burden under LDDAs and improve system performance subject to destabilizing switching. Then, an improved DT for switching signal permits irregular arrangement of destabilizing and stabilizing switching and is more suitable for NSSs subject to LDDAs. Moreover, sufficient conditions ensure the solvability of EORP for NSSs with USDs under LDDAs, network-induced delays, random packet losses, and packet disorders. Finally, a switched RLC circuit shows the feasibility of the proposed method.

This article investigates the sensitivity analysis (SA) of high-dimensional data to identify the effects of process variables on output quantity of interest (QoI) in industrial soft sensor modeling. The computational cost of analyzing the SA of high-dimensional data is high, and models available for SA techniques usually have limited generalization capacity. Therefore, we propose a novel high-dimensional data global SA (GSA) approach based on a deep soft sensor model to address these issues. We first develop an approximately incremental grouping (AIG) algorithm and a region-based cooperative co-evolution (RBCC) algorithm to decompose the high-dimensional data into independent regions for the GSA. Subsequently, a multihead deep soft sensor model with generalization performance is designed to determine the GSA indices of each decomposed region. Specifically, the region of interest (RoI) align algorithm provides the multihead with precisely located decomposed region features. Finally, based on the uncertainty analysis of each model head, we present a joint loss function with the Monte Carlo dropout (MC-dropout) algorithm to measure the GSA indices of each decomposed region on QoIs. Experimental evaluation results on a benchmark dataset and a real-world one demonstrate the effectiveness of the proposed approach in addressing the GSA of high-dimensional data in industrial processes.

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

This article investigates the distributed fuzzy adaptive containment control problem of stochastic nonlinear multiagent systems under a directed communication topology suffering denial-of-service (DoS) attacks. First, considering unknown stochastic disturbance and nonlinear characteristics for the followers, the mathematical models are modeled as It$\hat{o}$stochastic nonlinearity terms approximated through fuzzy logic systems. Second, the proposed dynamically adjusted event-triggered condition can effectively avoid inefficient transmission behavior. Moreover, an adaptive compensation protocol for input saturation is constructed to eliminate the effects of nonlinearities caused by input saturation. Finally, instead of the previous uniformly ultimately bounded containment control results, stability and asymptotic performance are guaranteed through valid reasonable adaptive control laws acquired through the backstepping control approach despite suffering DoS cyberattacks. Moreover, a simulation is given to verify the feasibility of the proposed method.

Aeroengine performance optimization rem- ains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation cost for an aeroengine is expensive. In reality, the aeroengine model has multiple information sources with different costs and accuracy. The different costs and accuracy of the multiple information sources should be traded off to guide the search for the optimal in a cost-efficient way. Therefore, we propose a multi-information-source framework for enabling efficient multiobjective Bayesian optimization. We construct the surrogate model with a multifidelity Gaussian process and choose the location–source pair with a modified acquisition function. Finally, we apply the proposed method to improve the performance indexes of the aeroengine, which confirms the efficiency of the proposed algorithm.

This article considers the effective handling of uncertainty for economic model-predictive control with feasibility and stability guarantees. First, a stable scenario-based economic model-predictive control strategy is proposed based on Lyapunov techniques. This control strategy optimizes over a sequence of control policies instead of a sequence of control inputs, so as to take feedback into account to reduce the conservativeness. More uncertainty information over the prediction horizon is incorporated by employing an augmented prediction model with a scenario tree describing the evolution of the uncertainty. Second, since the scenario tree structure inevitably increases the optimization problem size, a trained deep neural network, as an approximation function, is resorted to modeling the scenario-based economic model-predictive control feedback control law to make online implementation tractable. The effectiveness of this approximate controller is verified through the probabilistic validation technique. Finally, the feasibility and stability of this approximate scenario-based economic model-predictive control are addressed theoretically. An application of this proposed controller on wind energy conversion systems demonstrates its effectiveness.

This paper addresses the prescribed tracking performance control problem for uncertain steer-by-wire (SbW) systems with input nonlinearity (including dead-zone and actuator fault) and the limitation of controller-area-network (CAN) bandwidth. An adaptive interval type-2 fuzzy logic system (IT2 FLS) is introduced to approximate the lumped model uncertainty, and a switching event-triggering mechanism (ETM) is applied to save the communication resources. Combining the backstepping approach and barrier Lyapunov function techniques, a prescribed tracking performance control method is proposed for SbW systems, where the initial values of state errors are no longer required in the controller design. Theoretical analysis shows that the tracking error can converge to the predefined residual set within preset time instead of the time tending infinite, while the closed-loop system is semi-globally stable. Simulations and vehicle experiments are presented to verify the effectiveness of the proposed control method.

This article proposes new inverse reinforcement learning (RL) algorithms to solve our defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games are solved by extracting the unknown cost function of an expert by a learner using demonstrated expert's behaviors. We first develop a model-based inverse RL algorithm that consists of two learning stages: an optimal control learning and a second learning based on inverse optimal control. This algorithm also clarifies the relationships between inverse RL and inverse optimal control. Then, we propose a new model-free integral inverse RL algorithm to reconstruct the unknown expert cost function. The model-free algorithm only needs online demonstration of the expert and learner's trajectory data without knowing system dynamics of either the learner or the expert. These two algorithms are further implemented using neural networks (NNs). In Adversarial Apprentice Games, the learner and the expert are allowed to suffer from different adversarial attacks in the learning process. A two-player zero-sum game is formulated for each of these two agents and is solved as a subproblem for the learner in inverse RL. Furthermore, it is shown that the cost functions that the learner learns to mimic the expert's behavior are stabilizing and not unique. Finally, simulations and comparisons show the effectiveness and the superiority of the proposed algorithms.

In inverse reinforcement learning (RL), there are two agents. An expert target agent has a performance cost function and exhibits control and state behaviors to a learner. The learner agent does not know the expert's performance cost function but seeks to reconstruct it by observing the expert's behaviors and tries to imitate these behaviors optimally by its own response. In this article, we formulate an imitation problem where the optimal performance intent of a discrete-time (DT) expert target agent is unknown to a DT Learner agent. Using only the observed expert's behavior trajectory, the learner seeks to determine a cost function that yields the same optimal feedback gain as the expert's, and thus, imitates the optimal response of the expert. We develop an inverse RL approach with a new scheme to solve the behavior imitation problem. The approach consists of a cost function update based on an extension of RL policy iteration and inverse optimal control, and a control policy update based on optimal control. Then, under this scheme, we develop an inverse reinforcement Q-learning algorithm, which is an extension of RL Q-learning. This algorithm does not require any knowledge of agent dynamics. Proofs of stability, convergence, and optimality are given. A key property about the nonunique solution is also shown. Finally, simulation experiments are presented to show the effectiveness of the new approach.

static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed algorithms lies in a newly developed OPFB control algorithm form for completely unknown systems. Under the premise of satisfying disturbance attenuation conditions, the conditions for the existence of the optimal OPFB solution are given. The convergence of the proposed Q -learning methods, and the difference and equivalence of two algorithms are rigorously proven. Moreover, considering the effects brought by probing noise for the persistence of excitation (PE), the proposed off-policy Q -learning method has the advantage of being immune to probing noise and avoiding biasedness of solution. Simulation results are presented to verify the effectiveness of the proposed approaches.

This article is concerned with the global prescribed performance tracking control problem for strict-feedback systems with quantized references, unknown nonlinearities, and unmatched disturbances. Discontinuity appears frequently in such references, for which most nonlinear control or filtering methods are not applicable straightforward. Besides, the existing approaches of predefining tracking performance work under local initial conditions, available nonlinearity knowledge, disturbance-free cases, or have complexity issues. In this article, a novel smoothing function is first designed for the online automatic generation of smooth trajectories in place of the quantized reference. Then, the tangent barrier functions are combined with a new form of performance functions (inverse proportional functions) to form a control. The strong robustness of the resulting controller against model uncertainties and disturbances evades the need for approximation, observation, etc., yielding simplicity of the control. The new performance functions relax the specific initial condition. Their combination ensures for any initial condition, the tracking error converges to a given bound. Simulation results on vehicular platoons illustrate the theoretical findings.

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rarely solve both challenges in a unified framework. In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques. First, we analytically show that the essential issue behind the large disparity and non-Lambertian challenges is the aliasing problem. Classic LF rendering approaches typically mitigate the aliasing with a reconstruction filter in the Fourier domain, which is, however, intractable to implement within a deep learning pipeline. Instead, we introduce an alternative framework to perform anti-aliasing reconstruction in the image domain and analytically show comparable efficacy on the aliasing issue. To explore the full potential, we then embed the anti-aliasing framework into a deep neural network through the design of an integrated architecture and trainable parameters. The network is trained through end-to-end optimization using a peculiar training set, including regular LFs and unstructured LFs. The proposed deep learning pipeline shows a substantial superiority in solving both the large disparity and the non-Lambertian challenges compared with other state-of-the-art approaches. In addition to the view interpolation for an LF, we also show that the proposed pipeline also benefits light field view extrapolation.

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods.Note to Practitioners—The extremely complicated dynamics of blast furnace ironmaking process make the model-based controllers difficult to realize in practice. In this article, a novel robust model-free adaptive predictive control method is proposed for direct data-driven control of multivariate molten iron quality in the ironmaking process. This method directly uses the process input and output data to design the multivariable quality controller online by the compact-form dynamic linearization technology and the internal multilayer prediction mechanism, thus avoids the drawback of model-based controllers in troublesome process modeling. Moreover, the proposed method can effectively avoid the influence of data loss and measurement noise on the controller performance with the designed Kalman filter-based robust mechanism. The superiority and practicability of the proposed method are verified using various experiments against actual industrial data.

This article studies an operational optimization problem of the fluid catalytic cracking (FCC) unit under uncertainty. The objective of this problem is to quickly reoptimize the operating variables that control the operational condition of the FCC unit when fossil fuel yield constraints or prices change. To solve this problem, based on the challenges caused by the varied constraints, we establish a mathematical model and propose a fast adaptive differential evolution algorithm with an adaptive mutation strategy, a parameter adaptation strategy, a repaired strategy, and an enhanced strategy. In the proposed algorithm, we integrate the status information of each solution into the mutation strategy and parameter adaptation scheme to search for the best solution in the irregular feasible region of the operating variables. In addition, a repaired strategy is proposed to repair the infeasible operating variables with unknown bounds, and an enhanced strategy is presented to further improve the objective function value of the best solution. The experimental results on ten test scenarios with different fossil fuel yield constraints and prices demonstrate the robustness of the proposed algorithm for optimizing the operating variables of the FCC unit under uncertainty.

In this article, for a class of continuous-time nonlinear nonaffine systems with unknown dynamics, a robust approximate optimal tracking controller (RAOTC) is proposed in the framework of adaptive dynamic programming (ADP). The distinguishing contribution of this article is that a new Lyapunov function is constructed, by using which the derivative information of tracking errors is not required in computing its time derivative along with the solution of the closed-loop system. Thus, the proposed method can make the system states follow nondifferentiable reference signals, which removes the common assumption that the reference signals have to be continuous for tracking control of continuous-time nonlinear systems in the literature. The theoretical analysis, simulation, and application results well illustrate the effectiveness and superiority of the proposed method.

This work studies the${H}_{\infty }$-based minimal energy control with a preset convergence rate (PCR) problem for a class of disturbed linear time-invariant continuous-time systems with matched external disturbance. This problem aims to design an optimal controller so that the energy of the control input satisfies a predetermined requirement. Moreover, the closed-loop system asymptotic stability with PCR is ensured simultaneously. To deal with this problem, a modified game algebraic Riccati equation (MGARE) is proposed, which is different from the game algebraic Riccati equation in the traditional${H}_{\infty } $control problem due to the state cost being lost. Therefore, a unique positive-definite solution of the MGARE is theoretically analyzed with its existing conditions. In addition, based on this formulation, a novel approach is proposed to solve the actuator magnitude saturation problem with the system dynamics being exactly known. To relax the requirement of the knowledge of system dynamics, a model-free policy iteration approach is proposed to compute the solution of this problem. Finally, the effectiveness of the proposed approaches is verified through two simulation examples.

Motivated by the guaranteed stability margins of linear quadratic regulators (LQRs) and standard Kalman filter (KF) in the frequency domain, this article extends these results to the distributed Kalman-consensus filter (DKCF) for distributed estimation in sensor networks. In particular, we study the robustness margins of DKCF in two cases, one of which is based on the direct target observation while the other uses estimates from neighbor sensors in the network. The loop transfer functions of the two cases are established, and gain and phase margin robustness results are derived for both. The robustness margins of DKCF are improved compared to the single-agent KF. Furthermore, as communication topology varies in sensor networks, graph overall coupling strengths change. We also analyze the correlation between overall coupling strengths and the robustness margins of DKCF.

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

This article proposes robust inverse Q -learning algorithms for a learner to mimic an expert's states and control inputs in the imitation learning problem. These two agents have different adversarial disturbances. To do the imitation, the learner must reconstruct the unknown expert cost function. The learner only observes the expert's control inputs and uses inverse Q -learning algorithms to reconstruct the unknown expert cost function. The inverse Q -learning algorithms are robust in that they are independent of the system model and allow for the different cost function parameters and disturbances between two agents. We first propose an offline inverse Q -learning algorithm which consists of two iterative learning loops: 1) an inner Q -learning iteration loop and 2) an outer iteration loop based on inverse optimal control. Then, based on this offline algorithm, we further develop an online inverse Q -learning algorithm such that the learner mimics the expert behaviors online with the real-time observation of the expert control inputs. This online computational method has four functional approximators: a critic approximator, two actor approximators, and a state-reward neural network (NN). It simultaneously approximates the parameters of Q -function and the learner state reward online. Convergence and stability proofs are rigorously studied to guarantee the algorithm performance.

Proportional, integral, and derivative (PID) feedback control, as a popular control law, plays a central role in industrial processes and traditional control applications. In the context of multiagent systems, one may also wonder what the fundamental capability and limitation of PID control may be. This article attempts to provide an answer from the viewpoint of consensus robustness against uncertain delay. We consider robust consensus of second-order unstable agents under PID feedback protocols, subject to a constant but unknown time delay over an undirected graph. The issue concerns the so-called delay consensus margin (DCM), which is the largest delay range within which robust consensus can be achieved. The specific problem under study investigates the role of integral control on the robust consensus, seeking to understand whether integral control can be employed to enhance consensus robustness. Our result shows that there is none; that is, in a PID protocol, the integral control action has no improving effect on the DCM, and that PID and proportional-derivative (PD) protocols achieve the same DCM. As a byproduct of this finding, the DCM under PID and PD protocols is found to be computable by solving a quasiconcave, albeit nonsmooth, unimodal optimization problem.

This article provides a novel inverse reinforcement learning (RL) algorithm that learns an unknown performance objective function for tracking control. The algorithm combines three steps: 1) an optimal control update; 2) a gradient descent correction step; and 3) an inverse optimal control (IOC) update. The new algorithm clarifies the relation between inverse RL and IOC. It is shown that the reward weight of an unknown performance objective that generates a target control policy may not be unique. We characterize the set of all weights that generate the same target control policy. We develop a model-based algorithm and, further, two model-free algorithms for systems with unknown model information. Finally, simulation experiments are presented to show the effectiveness of the proposed algorithms.

This article investigates a distributed fractional-order fault-tolerant formation-containment control (FOFTFCC) scheme for networked unmanned airships (UAs) to achieve safe observation of a smart city. In the proposed control method, an interval type-2 fuzzy neural network (IT2FNN) is first developed for each UA to approximate the unknown term associated with the loss-of-effectiveness faults in the distributed error dynamics, and then a disturbance observer (DO) is proposed to compensate for the approximation error and bias fault encountered by each UA, such that the composite learning strategy composed of the IT2FNN and the DO is obtained for each UA. Moreover, fractional-order (FO) calculus is incorporated into the control scheme to provide an extra degree of freedom for the parameter adjustments. The salient feature of the proposed control scheme is that the composite learning algorithm and FO calculus are integrated to achieve a satisfactory fault-tolerant formation-containment control performance even when a portion of leader/follower UAs is subjected to the actuator faults in a distributed communication network. Furthermore, it is shown by Lyapunov stability analysis that all leader UAs can track the virtual leader UA with time-varying offset vectors, and all follower UAs can converge into the convex hull spanned by the leader UAs. Finally, comparative hardware-in-the-loop (HIL) experimental results are presented to show the effectiveness and superiority of the proposed method.

For dynamic multiobjective optimization problems (DMOPs), it is challenging to track the varying Pareto-optimal front. Most traditional approaches estimate the Pareto-optimal sets in the decision space. However, the obtained solutions do not necessarily satisfy the desired properties of decision makers in the objective space. Inverse model-based algorithms have a great potential to solve such problems. Nonetheless, the existing ones have low precision for handling DMOPs with nonlinear correlations between the objective and decision vectors, which greatly limits the application of the inverse models. In this article, an inverse Gaussian process (IGP)-based prediction approach for solving DMOPs is proposed. Unlike most traditional approaches, this approach exploits the IGP to construct a predictor that maps the historical optimal solutions from the objective space to the decision space. A sampling mechanism is developed for generating sample points in the objective space. Then, the IGP-based predictor is employed to generate an effective initial population by using these sample points. The proposed method by introducing IGP can obtain solutions with better diversity and convergence in the objective space, which is more responsive to the demand of decision makers than the traditional methods. It also has better performance than other inverse model-based methods in solving nonlinear DMOPs. To investigate the performance of the proposed approach, experiments have been conducted on 23 benchmark problems and a real-world raw ore allocation problem in mineral processing. The experimental results demonstrate that the proposed algorithm can significantly improve the dynamic optimization performance and has certain practical significance for solving real-world DMOPs.

This article addresses the event-triggered output feedback control problem of steer-by-wire (SbW) systems subject to uncertain nonlinearity and time-varying disturbance. First, to solve the uncertainty and remove unnecessary sensors, an interval 2 fuzzy logic system and an adaptive state observer are proposed to estimate the uncertain nonlinearity and unavailable states of SbW systems. Then, an event-triggered output feedback control method is constructed for SbW systems to achieve the prespecified tracking performance. Much significantly, the jumping phenomenon of the control input caused by event-triggering communication can be eliminated by the proposed event-triggered control systems. Furthermore, theoretical analysis shows that the tracking error can converge to the preset neighborhood of origin within finite time, while the Zeno behavior can be avoided. Finally, simulations and vehicle experiments are presented to verify the validity of the proposed methods.

Polymer electrolyte membrane fuel cell (PEMFC) air supply systems are usually affected negatively by model uncertainties, external disturbance, and unmeasured variables. In this article, we propose a composite adaptive type-2 fuzzy controller based on a high-gain observer and a disturbance observer for oxygen excess ratio (OER) of PEMFC air supply systems. First, the derivatives of system output, which are unavailable due to limited sensors, are estimated via the high-gain observer. Then, interval type-2 fuzzy logic systems (IT2 FLSs) are adopted to approximate the unknown system dynamics and the disturbance observer is designed to estimate compound disturbance including unknown external disturbance and fuzzy approximation error. Finally, in order to improve the tracking performance, two composite adaptive updating laws are constructed by utilizing the estimated tracking error and the modeling error. Theoretical analysis shows that the system tracking error is uniformly ultimately bounded by Lyapunov stability theory. Numerical simulations and hardware-in-loop experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

This article proposes a fractional-order intelligent fault-tolerant synchronization tracking control (FO-I-FTSTC) scheme for multiple unmanned airships (UAs) against actuator faults. Within the developed control architecture, fixed-time prescribed performance functions (PPFs) are first designed to transform the synchronization tracking errors into a new set of error variables, such that the original errors are strictly confined within the prescribed bounds. Then, fractional calculus and sliding mode surface are sequentially introduced to construct the FO errors. Moreover, to handle the unknown terms and bias faults in the FO sliding-mode error dynamics, fuzzy neural networks with recurrent loops are artfully constructed to act as the intelligent learning units. Furthermore, the norm of the loss-of-effectiveness fault factors is introduced for each UA to reduce the number of adaptive parameters. The distinct feature of the proposed method is that the FO-I-FTSTC performance is significantly enhanced by integrating recurrent fuzzy neural networks, fractional calculus, and fixed-time PPFs into a unified framework, leading to a high-precision control scheme. It is shown by Lyapunov analysis that all UAs can track their desired references in a synchronized manner, and the synchronization tracking errors are bounded and strictly confined within the prescribed error bounds. Comparative hardware-in-the-loop experiments are presented to show the effectiveness of the proposed FO-I-FTSTC scheme.

Decomposition algorithms for large-scale refinery scheduling problems commonly adopt the spatially dividing method. When the scheduling horizon further enlarges, the sizes of the resulting subproblems exponentially grow, leading to the performance degradation of the decomposition algorithms. In this article, to solve this issue about subproblem size caused by the long-time scheduling horizon, a knowledge transfer based algorithm is proposed, where the size of subproblems is constant and a novel concept of product flowrate is introduced to transfer operation knowledge from tractable small-scale short-time problems to intractable large-scale long-time problems to quickly obtain a satisfactory solution for large-scale problems. Experimental results show that the proposed algorithm can achieve better solutions within 10−44 s for large-scale refinery scheduling problems with 50−200 time slots (the existing model is with less than 30 time slots), compared to the CPLEX solver with a running time of 3600 s and the existing evolutionary algorithm with a running time of 100−500 s. The results also show that, at present, the heuristic method (the essence of the proposed algorithm) is the main tool to solve large-scale combinatorial optimization problems, and even in the CPLEX solver, the heuristic is also the most active part.

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

This article investigates adaptive robust controller design for discrete-time (DT) affine nonlinear systems using an adaptive dynamic programming. A novel adaptive interleaved reinforcement learning algorithm is developed for finding a robust controller of DT affine nonlinear systems subject to matched or unmatched uncertainties. To this end, the robust control problem is converted into the optimal control problem for nominal systems by selecting an appropriate utility function. The performance evaluation and control policy update combined with neural networks approximation are alternately implemented at each time step for solving a simplified Hamilton-Jacobi-Bellman (HJB) equation such that the uniformly ultimately bounded (UUB) stability of DT affine nonlinear systems can be guaranteed, allowing for all realization of unknown bounded uncertainties. The rigorously theoretical proofs of convergence of the proposed interleaved RL algorithm and UUB stability of uncertain systems are provided. Simulation results are given to verify the effectiveness of the proposed method.

To address the architecture complexity and ill-posed problems of neural networks when dealing with high-dimensional data, this article presents a Bayesian-learning-based sparse stochastic configuration network (SCN) (BSSCN). The BSSCN inherits the basic idea of training an SCN in the Bayesian framework but replaces the common Gaussian distribution with a Laplace one as the prior distribution of the output weights of SCN. Meanwhile, a lower bound of the Laplace sparse prior distribution using a two-level hierarchical prior is adopted based on which an approximate Gaussian posterior with sparse property is obtained. It leads to the facilitation of training the BSSCN, and the analytical solution for output weights of BSSCN can be obtained. Furthermore, the hyperparameter estimation process is derived by maximizing the corresponding lower bound of the marginal likelihood function based on the expectation-maximization algorithm. In addition, considering the uncertainties caused by both noises in the real-world data and model mismatch, a bootstrap ensemble strategy using BSSCN is designed to construct the prediction intervals (PIs) of the target variables. The experimental results on three benchmark data sets and two real-world high-dimensional data sets demonstrate the effectiveness of the proposed method in terms of both prediction accuracy and quality of the constructed PIs.

This article investigates the distributed fault-tolerant time-varying formation control problem for multiple unmanned airships (UAs) against limited communication ranges and input saturation to achieve the safe observation of a smart city. To address the strongly nonlinear functions caused by the time-varying formation flight with limited communication ranges and bias faults, intelligent adaptive learning mechanisms are proposed by incorporating fuzzy neural networks. Moreover, Nussbaum functions are introduced to handle the input saturation and loss-of-effectiveness faults. The distinct features of the proposed control scheme are that time-varying formation flight, actuator faults including bias and loss-of-effectiveness faults, limited communication ranges, and input saturation are simultaneously considered. It is proven by Lyapunov stability analysis that all UAs can achieve a safe formation flight for the smart city observation even in the presence of actuator faults. Hardware-in-the-loop experiments with open-source Pixhawk autopilots are conducted to show the effectiveness of the proposed control scheme.

Gaussian processes (GPs) are widely used in surrogate-assisted evolutionary optimization of expensive problems mainly due to the ability to provide a confidence level of their outputs, making it possible to adopt principled surrogate management methods, such as the acquisition function used in the Bayesian optimization. Unfortunately, GPs become less practical for high-dimensional multiobjective and many-objective optimization as their computational complexity is cubic in the number of training samples. In this article, we propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multiobjective and many-objective expensive optimization problems. While the conventional dropout neural network needs to save a large number of network models during the training for calculating the confidence level, only one single network model is needed in the EDN to estimate the fitness and its confidence level by randomly ignoring neurons in both training and testing the neural network. Extensive experimental studies on benchmark problems with up to 100 decision variables and 20 objectives demonstrate that, compared to state of the art, the proposed algorithm is not only highly competitive in performance but also computationally more scalable to high-dimensional many-objective optimization problems. Finally, the proposed algorithm is validated on an operational optimization problem of crude oil distillation units, further confirming its capability of handling expensive problems given a limited computational budget.

This article considers the decentralized fractional-order fault-tolerant control problem for unmanned aerial vehicles (UAVs) against wind disturbances and actuator faults in a directed communication network. A new composite adaptive disturbance observer-based decentralized fractional-order fault-tolerant control (CADOB-DFO-FTC) scheme, which incorporates fractional-order (FO) sliding-mode surfaces, nonlinear disturbance observers (NDOs), fuzzy wavelet neural networks (FWNNs), and robust controllers, is developed to achieve the attitude tracking control of networked UAVs in a decentralized way. Based on the FO sliding-mode surfaces, the NDOs are first developed to estimate the lumped uncertainties due to the aerodynamic parameter perturbations, wind disturbances, and actuator faults. Then, adaptive FWNNs with updating weighting matrices, mean vectors, and deviation vectors are constructed to effectively attenuate the adverse effects induced by the NDO estimation errors. Furthermore, to compensate the FWNN approximation errors, robust controllers are integrated into the developed control scheme to enhance the approximation abilities. It is shown that by using Lyapunov methods, all UAVs can track their attitude references. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed method.

This article is dealt with the problem of trajectory tracking with prescribed performance for a family of underactuated surface vessels (USVs) under model uncertainties and disturbances. The prescribed performance means that the USV tracks a given trajectory with the arbitrarily predefined speed of response and accuracy. The existing prescribed performance control (PPC) solutions and the traditional robust control approaches for USVs may have the singularity issue or cause a discontinuous control signal. Thereby, a new-type adaptive PPC strategy is put forward in this article. The adaptive technique is devoted to tackling model imperfections as usual, whereas the constraint-handling technique is adopted in a novel way. Herein, we first construct an auxiliary variable instead of using the approach angle or azimuth angle. Then, we impose constraints on the position error, not the tracking error, and the auxiliary variable, simultaneously. In this way, the predefined performance is achieved by moreover a singularity-free continuous control action. These theoretical findings are illustrated via a comparative simulation study.

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an $\mathcal{O}(T^{\max\{c,1-c\}})$ bound for static regret and an $\mathcal{O}(T^{(1-c)/2})$ bound for cumulative constraint violation, where $c\in(0,1)$ is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to $\mathcal{O}(\log(T))$ when the loss functions are strongly convex, which also improves existing results. %In order to bound the regret with respect to any comparator sequence, In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal $\mathcal{O}(\sqrt{T(1+P_T)})$ regret and an $\mathcal{O}(\sqrt{T})$ cumulative constraint violation, where $P_T$ is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

To exploit the power domain diversity, heterogeneous non-orthogonal multiple access (NOMA) networks improve the spectrum efficiency for Internet of Things (IoT). For video traffic transmission, we formulate a resource allocation and assignment problem as a mixed integer non-linear programming (MINLP) subject to video encoding characteristics, maximum number of accessed devices, max-min fairness criterion, and total available energy of each device. To solve the resource allocation and assignment problem over heterogeneous NOMA networks, two subproblems are formulated, i.e., a packet assignment subproblem for video traffic and a joint device allocation and power control subproblem. Firstly, the joint device allocation and power control subproblem is transformed into a bi-convex programming with successive convex approximation (SCA) method. Then, an optimal device allocation and power control solution is obtained via dual decomposition method. Finally, a heuristic packet assignment algorithm via greedy method is presented for video transmission traffic. In numerical simulation, we can see that the proposed algorithm guarantees the max-min fairness among different devices, and improves the minimum and average video transmission quality over heterogeneous NOMA networks.

The electricity demand of the fused magnesia smelting process (FMSP) is defined as the average electric power consumption over a fixed period of time, which is used to monitor the electricity cost in the FMSP. In this article, we develop a dynamic model of the electricity demand based on the closed-loop control system of the smelting current in the FMSP. The electricity demand prediction model combines an identifiable linear model with an unknown nonlinear dynamic system, which takes advantage of system identification. To predict the unknown nonlinear dynamic system, an adaptive deep learning prediction approach is proposed based on a multilayer long short-term memory. The real data in the FMSP is used to verify the effectiveness of the proposed electricity demand forecasting method.

Complex industrial processes are commonly characterized with multiple operation modes. The existing manifold learning-based process monitoring methods describe each mode individually without capturing the connections among different modes, which may deteriorate the monitoring capability. This article proposes a novel dimensionality reduction model referred as to multimanifold joint projections to monitor the multimode processes, where the intramode and the intermode adjacency matrices are constructed to reflect the underlying features within each mode and among different modes, respectively. The neighboring and nonneighboring structures of data within each mode are captured by the distance and angle information of pairwise points to reveal the intrinsic structure of the original data, thus offering a more faithful representation of multimodal data and further enhancing monitoring performance. During online monitoring, a point to manifold distance criterion is proposed to determine the running-on mode of new samples. Two case studies demonstrated the superior performance of the proposed approach in multimode process monitoring.

Typical learning-based light field reconstruction methods demand in constructing a large receptive field by deepening their networks to capture correspondences between input views. In this paper, we propose a spatial-angular attention network to perceive non-local correspondences in the light field, and reconstruct high angular resolution light field in an end-to-end manner. Motivated by the non-local attention mechanism (Wang et al., 2018; Zhang et al., 2019), a spatial-angular attention module specifically for the high-dimensional light field data is introduced to compute the response of each query pixel from all the positions on the epipolar plane, and generate an attention map that captures correspondences along the angular dimension. Then a multi-scale reconstruction structure is proposed to efficiently implement the non-local attention in the low resolution feature space, while also preserving the high frequency components in the high-resolution feature space. Extensive experiments demonstrate the superior performance of the proposed spatial-angular attention network for reconstructing sparsely-sampled light fields with Non-Lambertian effects.

In railway operation, train delays may occur due to various reasons (e.g. severe weather, infrastructure failure, human factors, etc.) and may change and spread rapidly to subsequent trains. Prediction of train delay changes and propagation is important to provide decision-making support for railway dispatchers to reschedule. In this paper, a multi-stage intelligent method is proposed for predicting the dynamic changes and propagation of train delays using random vector functional-link networks (RVFLNs) with improved transfer learning and ensemble learning. First, to improve the prediction performance of the single RVFLNs model, a novel improved Stacking ensemble learning RVFLNs (SRN) regression algorithm is proposed for prediction modeling of the delay changes. Then, to ensure the classification accuracy of unlabeled and class-imbalanced train data, an improved transfer learning RVFLNs (ITRN) classifier is proposed to decide whether the initial delay will lead to associated delays, with the aid of the improved SMOTE algorithm for processing imbalanced data. If associated delays are identified by the ITRN classifier, the proposed SRN algorithm will be further to predict the subsequent associated delays. By iterating the classification and prediction procedures, the propagation range and chain of train delays can be obtained. The effectiveness and practicability of the proposed method are verified by using two experiments against actual train graph data from different railway lines.

Dense medium separation (DMS) is of great significance for coal cleaning. The DMS control system always involves dense medium density adjustment and ash content control that are operating on fast and slow time scales, respectively. The inherent time-varying and strongly nonlinear characteristics of the DMS process give rise to challenges for the design of this multitime scale control system. To address this issue, this article proposes a dual-rate adaptive optimal tracking control approach for the DMS system. For the basic loop process, a nonlinear adaptive PI controller containing a neural network (NN)-based unmodeled dynamics compensator is proposed. Then, a lifting technique is used to unify the time scales of the two loops accompanied by formulating a generalized controlled object, whose dynamics is completely unknown. On this basis, a data-driven operation optimization control method that combines adaptive dynamic programming algorithm and reference control is developed, which is implemented using NNs. Finally, the stability of the proposed method is analyzed. The simulation results indicate its effectiveness.

This article applies a singular perturbation theory to solve an optimal linear quadratic tracker problem for a continuous-time two-time-scale process. Previously, singular perturbation was applied for system regulation. It is shown that the two-time-scale tracking problem can be separated into a linear-quadratic tracker (LQT) problem for the slow system and a linear-quadratic regulator (LQR) problem for the fast system. We prove that the solutions to these two reduced-order control problems can approximate the LQT solution of the original control problem. The reduced-order slow LQT and fast LQR control problems are solved by off-policy integral reinforcement learning (IRL) using only measured data from the system. To test the effectiveness of the proposed method, we use an industrial thickening process as a simulation example and compare our method to a method with the known system model and a method without time-scale separation.

This article presents an enhanced fault-tolerant synchronization tracking control scheme using fractional-order (FO) calculus and intelligent learning architecture for networked fixed-wing unmanned aerial vehicles (UAVs) against actuator and sensor faults. To increase the flight safety of networked UAVs, a recurrent wavelet fuzzy neural network (RWFNN) learning system with feedback loops is first designed to compensate for the unknown terms induced by the inherent nonlinearities, unexpected actuator, and sensor faults. Then, FO sliding-mode control (FOSMC), involving the adjustable FO operators and the robustness of SMC, are dexterously proposed to further enhance flight safety and reduce synchronization tracking errors. Moreover, the dynamic parameters of the RWFNN learning system embedded in the networked fixed-wing UAVs are updated based on adaptive laws. Furthermore, the Lyapunov analysis ensures that all fixed-wing UAVs can synchronously track their references with bounded tracking errors. Finally, comparative simulations and hardware-in-the-loop experiments are conducted to demonstrate the validity of the proposed control scheme.

To achieve plant-wide operational optimization and dynamic adjustment of operational index for an industrial process, knowledge-based methods have been widely employed over the past years. However, the extraction of knowledge base is a bottleneck for most existing approaches. To address this problem, we propose a novel framework based on the generative adversarial networks (GANs), termed as decision-making GAN (DMGAN), which directly learns from operational data and performs human-level decision making of the operational indices for plant-wide operation. In the proposed DMGAN, two adversarial criteria and three cycle consistency criteria are incorporated to encourage efficient posterior inference. To improve the generalization power of a generator with an increasing complexity of the industrial processes, a reinforced U-Net (RU-Net) is presented that improves the traditional U-Net by providing a more general combinator, a building block design, and drop-level regularization. In this article, we also propose three quantitative metrics for assessing the plant-wide operation performance. A case study based on the largest mineral processing factory in Western China is carried out, and the experimental results demonstrate the promising performance of the proposed DMGAN when compared with decision-making based on domain experts.

This paper presents a model-free optimal approach based on reinforcement learning for solving the output regulation problem for discrete-time systems under disturbances. This problem is first broken down into two optimization problems: 1) a constrained static optimization problem is established to find the solution to the output regulator equations (i.e., the feedforward control input) and 2) a dynamic optimization problem is established to find the optimal feedback control input. Solving these optimization problems requires the knowledge of the system dynamics. To obviate this requirement, a model-free off-policy algorithm is presented to find the solution to the dynamic optimization problem using only measured data. Then, based on the solution to the dynamic optimization problem, a model-free approach is provided for the static optimization problem. It is shown that the proposed algorithm is insensitive to the probing noise added to the control input for satisfying the persistence of excitation condition. Simulation results are provided to verify the effectiveness of the proposed approach.

This article presents a novel technique to achieve plant-wide performance optimization for large-scale unknown industrial processes by integrating the reinforcement learning method with the multiagent game theory. A main advantage of this technique is that plant-wide optimal performance is achieved by a distributed approach where multiple agents solve simplified local nonzero-sum optimization problems so that a global Nash equilibrium is reached. To this end, first, the plant-wide performance optimization problem is reformulated by decomposition into local optimization subproblems for each production index in a multiagent framework. Then, the nonzero-sum graphical game theory is utilized to compute the operational indices for each unit process with the purpose of reaching the global Nash equilibrium, resulting in production indices following their prescribed target values. The stability and the global Nash equilibrium of this multiagent graphical game solution are rigorously proved. The reinforcement learning methods are then developed for each agent to solve the nonzero-sum graphical game problem using data measurements available in the system in real time. The plant dynamics do not have to be known. Finally, the emulation results are given to show the effectiveness of the proposed automated decision algorithm by using measured data from a large mineral processing plant in Gansu Province, China.

By dealing with robust modeling and online learning together in a unified random vector functional-link networks (RVFLNs) framework, this paper presents a novel robust online sequential RVFLNs for data modeling of dynamic time-varying systems together with its application for a blast furnace (BF) ironmaking process. First, to overcome the difficulties caused by the nonlinear time-varying dynamics of process and to enable the RVFLNs to learn online and to avoid data saturation, an improved online sequential version of RVFLNs (OS-RVFLNs) is presented by sequential learning with forgetting factor. It has been shown that the improved OS-RVFLNs with forgetting factor is not only suitable for the large-scale and real-time data transfer situation but also can adjust the sensitivity of the algorithm to different samples. Second, in order to solve the issue of modeling robustness when the dataset is contaminated with various outliers, a Cauchy distribution function weighted M-estimator is introduced to strengthen the robustness of the improved OS-RVFLNs. The non-Gaussian Cauchy distribution function is used to estimate the weights of different data and thus the corresponding contribution on modeling can be properly distinguished. Experiments using actual industrial data of a large BF ironmaking process have demonstrated that the proposed algorithm produces a much stronger robustness and better estimation accuracy than other algorithms.

To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.

In offline data-driven optimization, only historical data is available for optimization, making it impossible to validate the obtained solutions during the optimization. To address these difficulties, this paper proposes an evolutionary algorithm assisted by two surrogates, one coarse model and one fine model. The coarse surrogate (CS) aims to guide the algorithm to quickly find a promising subregion in the search space, whereas the fine one focuses on leveraging good solutions according to the knowledge transferred from the CS. Since the obtained Pareto optimal solutions have not been validated using the real fitness function, a technique for generating the final optimal solutions is suggested. All achieved solutions during the whole optimization process are grouped into a number of clusters according to a set of reference vectors. Then, the solutions in each cluster are averaged and outputted as the final solution of that cluster. The proposed algorithm is compared with its three variants and two state-of-the-art offline data-driven multiobjective algorithms on eight benchmark problems to demonstrate its effectiveness. Finally, the proposed algorithm is successfully applied to an operational indices optimization problem in beneficiation processes.

The dynamic economic optimization of the ultrasupercritical (USC) boiler-turbine unit has become an important task in modern power plants. Economic model predictive control (EMPC) has recently developed to be a promising method for realizing the dynamic economy. This EMPC essentially requires a highly reliable model for USC dynamic prediction which could reflect the internal mechanism of USC with big data feature. This article constitutes a deep-neural-network-based EMPC for the USC unit. Deep belief network (DBN) is used to model the USC unit with mathematical structure. To overcome the nonlinearity and time delay existing in the pulverized channel, an augmented model with predictor embedded is also incorporated into the EMPC design. The auxiliary controller and stability region have been constituted to guarantee closed-loop stability. Simulation results on a 1000-MW USC unit fully demonstrate the effectiveness of the proposed DBN-based EMPC.

This article investigates the use of a mesoscale kinetic model to cooperate with the operational optimal control of bauxite ore grinding process. In this article, we propose a new modeling framework where a discretized distributed parameter macroscale model and a mesoscale kinetic model are combined to predict the grinding product particle size. The mesoscale kinetic method does not need an explicit model of the process because it describes the process as a stochastic process. However, the high computational demand has prevented the kinetic model from using an online setting. We overcome this problem by embedding an acceleration algorithm based on the τ-leap method. The proposed model is validated using experimental data. Finally, a solution of the bauxite ore grinding operational optimal control is proposed and the cooperation of the predictive model with other modular is demonstrated.

Current challenges in industrial processes control include achieving optimum operation for systems with two-time-scale dynamics and unknown models. This paper presents, for the first time, the integration of singular perturbation theory and reinforcement learning to solve this problem. To this end, an optimal operational control (OOC) problem with two time scales is formulated to reach the desired operational indices. Then, a singularly perturbed dynamics for two-time-scale industrial operational processes is developed by introducing a perturbed scale, resulting in the separation of the original system dynamics. Thus, the original optimization problem is decomposed into a reduced slow subproblem and a boundary fast subproblem. The fact that the sum of the separate solutions of these subproblems is approximately equal to the solution of the OOC problem is proven. Then, two Q-learning algorithms are proposed to obtain a composite feedback control. Finally, an industrial thickener example is employed to show the effectiveness of the proposed method.

Obtaining accurate point prediction of industrial processes' key variables is challenging due to the outliers and noise that are common in industrial data. Hence the prediction intervals (PIs) have been widely adopted to quantify the uncertainty related to the point prediction. In order to improve the prediction accuracy and quantify the level of uncertainty associated with the point prediction, this article estimates the PIs by using ensemble stochastic configuration networks (SCNs) and bootstrap method. The estimated PIs can guarantee both the modeling stability and computational efficiency. To encourage the cooperation among the base SCNs and improve the robustness of the ensemble SCNs when the training data are contaminated with noise and outliers, a simultaneous robust training method of the ensemble SCNs is developed based on the Bayesian ridge regression and M-estimate. Moreover, the hyperparameters of the assumed distributions over noise and output weights of the ensemble SCNs are estimated by the expectation-maximization (EM) algorithm, which can result in the optimal PIs and better prediction accuracy. Finally, the performance of the proposed approach is evaluated on three benchmark data sets and a real-world data set collected from a refinery. The experimental results demonstrate that the proposed approach exhibits better performance in terms of the quality of PIs, prediction accuracy, and robustness.

In this article, a new model-free approach is proposed to solve the output regulation problem for networked control systems, where the system state can be lost in the feedback process. The goal of the output regulation is to design a control law that can make the system achieve asymptotic stability of the tracking error while maintaining the stability of the closed-loop system. The solvability of the output regulation problem depends on the solvability of a set of matrix equations called the regulator equations. First, a restructured dynamic system is established by using the Smith predictor; then, an off-policy algorithm based on reinforcement learning is developed to calculate the feedback gain using only the measured data when dropout occurs. Based on the solution to the feedback gain, a model-free solution is provided for solving the forward gain using the regulator equations. The simulation results demonstrate the effectiveness of the proposed approach for discrete-time networked systems with unknown dynamics and dropout.

Motion tracking control design of nonholonomic mobile robot systems considering actuator dynamics is addressed in this paper. A trajectory tracking controller is designed at actuator level, which guarantees that the nonholonomic mobile robot tracks a given trajectory. A numerical example is shown to demonstrate and validate the proposed approach in this paper.

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. In this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. Finally, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of setpoint tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.

In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstruction can be efficiently modeled as angular restoration on an epipolar plane image (EPI). The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling. Directly upsampling or super-resolving the light field in the angular dimensions causes ghosting effects. To suppress these ghosting effects, we contribute a novel "blur-restoration-deblur" framework. First, the "blur" step is applied to extract the low-frequency components of the light field in the spatial dimensions by convolving each EPI slice with a selected blur kernel. Then, the "restoration" step is implemented by a CNN, which is trained to restore the angular details of the EPI. Finally, we use a non-blind "deblur" operation to recover the spatial high frequencies suppressed by the EPI blur. We evaluate our approach on several datasets, including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms. We further show extended applications, including depth enhancement and interpolation for unstructured input. More importantly, a novel rendering approach is presented by combining the proposed framework and depth information to handle large disparities.

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners-Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product.

In this paper, a security-aware energy-efficient resource allocation is modeled as a fractional programming problem for heterogeneous multi-homing networks. The security-aware resource allocation is formulated as a secrecy energy efficiency maximization problem subject to the average packet delay, the average packet dropping probability, and the total available power consumption. In order to guarantee the packet-level quality of service (QoS), first, the average packet delay and the average packet dropping probability requirement for each mobile terminal at the link layer are transformed into a minimum secrecy rate constraint at the physical layer. Then, the non-convex secrecy energy efficiency maximization problem is approximated by a convex problem through epigraph representation. A security-aware energy-efficient resource allocation algorithm is then proposed leveraging dual-decomposition method and bi-section search method. Finally, a heuristic security-aware resource allocation algorithm is proposed to serve as a benchmark. Simulation results demonstrate that the proposed security-aware energy-efficient resource allocation algorithm not only improves the secrecy energy efficiency and throughput, but also guarantees the packet-level QoS.

Gaussian processes (GPs) are the most popular model used in surrogate-assisted evolutionary optimization of computationally expensive problems, mainly because GPs are able to measure the uncertainty of the estimated fitness values, based on which certain infill sampling criteria can be used to guide the search and update the surrogate model. However, the computation time for constructing GPs may become excessively long when the number of training samples increases, which makes it inappropriate to use them as surrogates in evolutionary optimization. To address this issue, this paper proposes to use ensembles as surrogates and infill criteria for model management in evolutionary optimization. A heterogeneous ensemble consisting of a least square support vector machine and two radial basis function networks is constructed to enhance the reliability of ensembles for uncertainty estimation. In addition to the original decision variables, a selected subset of the decision variables and a set of transformed variables are used as inputs of the heterogeneous ensemble to further promote the diversity of the ensemble. The proposed heterogeneous ensemble is compared with a GP and a homogeneous ensemble for infill sampling criteria in evolutionary multiobjective optimization. Experimental results demonstrate that the heterogeneous ensemble is competitive in performance compared with GPs and much more scalable in computational complexity to the increase in search dimension.

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.

Operation performance of mineral grinding processes is measured by the grinding product particle size and the circulating load, as two of the most crucial operational indices that measure the product quality and operation efficiency, respectively. In this paper, a data-driven method is proposed for the operational control design of mineral grinding processes with input constraints. A reference governor is introduced to take into account the input constraints and the infeasible setpoint issue. The reference governor generates feasible setpoints that keep control inputs within allowed regions. The lookup table embedded in the reference governor mapping steady-state outputs to inputs provides feasible setpoints for output regulation and baseline for inputs. An ad hoc optimization guarantees that the input constraints are not violated, with the priority of regulating the grinding product particle size if regulation of both indices is not feasible. Since the dynamic model of the controlled plant is complicated because of the strongly nonlinear and intricately coupled nature of ball mills and hydrocyclones, a novel policy iteration algorithm is proposed for optimal regulator design without system modeling. Simulation results comparing performances of a mineral grinding process with and without the reference governor show the effectiveness of the proposed method.

Research in light field reconstruction focuses on synthesizing novel views with the assistance of depth information. In this paper, we present a learning-based light field reconstruction approach by fusing a set of sheared epipolar plane images (EPIs). We start by showing that a patch in a sheared EPI will exhibit a clear structure when the sheared value equals the depth of that patch. By taking advantage of this pattern, a convolutional neural network (CNN) is then trained to evaluate the sheared EPIs, and output a reference score for fusing the sheared EPIs. The proposed CNN is elaborately designed to learn the similarity degree between the input sheared EPI and the ground truth EPI. Therefore, no depth information is required for network training and reasoning. We demonstrate the high performance of the proposed method through evaluations on synthetic scenes, real-world scenes, and challenging microscope light fields. We also show a further application of our proposed network for depth inference.

In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving optimal control problem of affine nonlinear discrete-time (DT) systems, using only the measured data along the system trajectories. Affine nonlinear feature of systems, unknown dynamics, and off-policy learning approach pose tremendous challenges on approximating optimal controllers. To this end, on-policy Q-learning method for optimal control of affine nonlinear DT systems is reviewed first, and its convergence is rigorously proven. The bias of solution to Q-function-based Bellman equation caused by adding probing noises to systems for satisfying persistent excitation is also analyzed when using on-policy Q-learning approach. Then, a behavior control policy is introduced followed by proposing an off-policy Q-learning algorithm. Meanwhile, the convergence of algorithm and no bias of solution to optimal control problem when adding probing noise to systems are investigated. Third, three neural networks run by the interleaved Q-learning approach in the actor-critic framework. Thus, a novel off-policy interleaved Q-learning algorithm is derived, and its convergence is proven. Simulation results are given to verify the effectiveness of the proposed method.

This paper studies the operational optimal control problem for the industrial flotation process, a key component in the mineral processing concentrator line. A new model-free data-driven method is developed here for real-time solution of this problem. A novel formulation is given for the optimal selection of the process control inputs that guarantees optimal tracking of the operational indices while maintaining the inputs within specified bounds. Proper tracking of prescribed operational indices, namely concentrate grade and tail grade, is essential in the proper economic operation of the flotation process. The difficulty in establishing an accurate mathematic model is overcome, and optimal controls are learned online in real time, using a novel form of reinforcement learning we call interleaved learning for online computation of the operational optimal control solution. Simulation experiments are provided to verify the effectiveness of the proposed interleaved learning method and to show that it performs significantly better than standard policy iteration and value iteration.

This paper develops a new method for solving the optimal control tracking problem for networked control systems (NCSs), where network-induced dropout can occur and the system dynamics are unknown. First, a novel dropout Smith predictor is designed to predict the current state based on historical data measurements over the communication network. Then, it is shown that the quadratic form of the performance index is preserved even with dropout, and the optimal tracker solution with dropout is given based on a novel dropout generalized algebraic Riccati equation. New algorithms for off-line policy iteration (PI), online PI, and Q-learning PI are presented for NCS with dropout. The Q-learning algorithm adaptively learns the optimal control online using data measured over the communication network based on reinforcement learning, including dropout, without requiring any knowledge of the system dynamics. Simulation results are provided to show that the proposed approaches give proper optimal tracking performance for the NCS with unknown dynamics and dropout.

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a self-tuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.

In this paper, we take advantage of the clear texture structure of the epipolar plane image (EPI) in the light field data and model the problem of light field reconstruction from a sparse set of views as a CNN-based angular detail restoration on EPI. We indicate that one of the main challenges in sparsely sampled light field reconstruction is the information asymmetry between the spatial and angular domain, where the detail portion in the angular domain is damaged by undersampling. To balance the spatial and angular information, the spatial high frequency components of an EPI is removed using EPI blur, before feeding to the network. Finally, a non-blind deblur operation is used to recover the spatial detail suppressed by the EPI blur. We evaluate our approach on several datasets including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms. We also show a further application for depth enhancement by using the reconstructed light field.

Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.

This paper develops an off-policy reinforcement learning (RL) algorithm to solve optimal synchronization of multiagent systems. This is accomplished by using the framework of graphical games. In contrast to traditional control protocols, which require complete knowledge of agent dynamics, the proposed off-policy RL algorithm is a model-free approach, in that it solves the optimal synchronization problem without knowing any knowledge of the agent dynamics. A prescribed control policy, called behavior policy, is applied to each agent to generate and collect data for learning. An off-policy Bellman equation is derived for each agent to learn the value function for the policy under evaluation, called target policy, and find an improved policy, simultaneously. Actor and critic neural networks along with least-square approach are employed to approximate target control policies and value functions using the data generated by applying prescribed behavior policies. Finally, an off-policy RL algorithm is presented that is implemented in real time and gives the approximate optimal control policy for each agent using only measured data. It is shown that the optimal distributed policies found by the proposed algorithm satisfy the global Nash equilibrium and synchronize all agents to the leader. Simulation results illustrate the effectiveness of the proposed method.

While modeling nonlinear systems by combining a linear model with a nonlinear compensation term, namely, virtual unmodeled dynamics (VUD), the parameter estimation of the linear model and the learning-based VUD estimate influences and interacts with each other simultaneously. This paper aims to develop an alternating identification scheme for resolving such a challenging problem, where a projection algorithm is employed to identify the linear model and a feedforward neural network is used to model the VUD of a class of nonlinear dynamical systems. An open-loop estimation algorithm on the VUD is first presented under the known linear model, followed by an alternating identification algorithm for completely unknown nonlinear systems. Algorithm description is given and some simulation studies on multiple input and multiple output nonlinear systems are carried out to illustrate the effectiveness of our proposed modeling techniques.

Using evolutionary algorithms (EAs) to handle dynamic multiobjective optimization problems (DMOPs) is a challenging topic. In this paper, a prediction strategy based on reference points is proposed to improve the performance of EAs in solving DMOPs. The reference point based strategy is to partition the population into several subpopulations according to the reference points. When a change is detected, a sequence of the subpopulation centers in the previous environments belonging to the same reference point are used to estimate the center of in new environment. Based on the predicted centers, the EA will generate an initial population for the new environment using a combination of a Uniform distribution for enhancing population diversity and a Gaussian distributions for accelerating convergence. Experiments on ten test instances have been carried out to evaluate the performance of proposed strategy and the results show that reference point based prediction strategy exhibits superior performance in dealing with DMOPs with nonlinear correlations between decision variables and severe environmental changes.

Due to the non-renewable character, magnetic materials (especially rare earth permanent magnet) become a hot topic which world scientists are eager to explore and research. Magnetic enterprises are constantly promoting lean production. The core of lean production is production planning and scheduling, which is also the core of enterprise management. It is essential to effective control of plant inventory, improve production efficiency, reduce production cost and rise product delivery satisfaction rate. All above can be realized by using the fast and effective methods which come from multi-process coordination production planning and scheduling. A research method review of magnetic materials multi-process coordination production planning and scheduling is given on the base of describing magnetic materials production process with rare earth as the raw material. Finally, the paper gives four research fields of magnetic materials multi-process coordination production planning and scheduling.

Projection to latent structures (PLS) and concurrent PLS are approaches for solving quality-relevant process monitoring. In this paper, a new approach called concurrent kernel PLS (CKPLS) is presented to detect faults comprehensively for nonlinear processes. The new model divides the nonlinear process and quality spaces into five subspaces: the co-varying, process-principal, process-residual, quality-principal, and quality-residual subspaces. The co-varying subspace reflects nonlinear relationship between quality variables and original process variables. The process-principal and process-residual subspaces reflect the principal variations and residuals, respectively, in the nonlinear process space. Further, the quality-principal and quality-residual subspaces reflect the principal variations and residuals, respectively, in the quality space. The proposed approach is demonstrated by a numerical simulation and an application of the Tennessee Eastman process.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

This paper provides a method for automatically selecting optimal operational indices for unit processes in an industrial plant using measured data and without knowing dynamical models of the unit process. A dynamic multiobjective optimization problem is defined to find operational indices that lead to plant-wide production indices close to their target values. A case-based reasoning (CBR) technique is also employed, which uses the stored experience of a human expert to determine appropriate operational indices for given target production indices. The solutions of the optimization problem and CBR technique are combined to form baseline operational indices. The dynamic models of the production indices, however, are time varying and affected by disturbances and online corrections of these baseline operational indices are required. To this end, reinforcement learning (RL) is used to provide a data-driven optimization technique to compensate for disturbances and model approximation errors and variations. The data-driven RL approach is used in two different time scales. The samples of the predicted production indices are used at a fast sampling rate, i.e., at each sample time, and the samples of actual production indices are used at a slower sampling rate, i.e., after each operational run, to correct the baseline operational indices. The effectiveness of this automated decision procedure has been demonstrated by successful implementation of the proposed approach on a large mineral processing plant in Gansu Province, China.

Data-driven modeling based on the shell vibration and acoustic signals of ball mills is normally applied to overcome the subjective errors of human inference. Many previously proposed selective ensemble (SEN) modeling approaches are based on “the manipulation of input features” from the multiinformation fusion perspective, which cannot selectively and jointly fuse the information hidden in multiscale spectral features and under several operating conditions (training samples). Therefore, this study suggests a new soft measuring procedure based on ensemble empirical mode decomposition (EEMD) and SEN. An improved kernel partial least-squares algorithm for SEN that is based on “subsample training samples” is utilized to construct a soft measuring model with the selected features and training samples. This study compares such data-driven soft measuring methods. The comparative results of bootstrap-based prediction performance estimation show that different methods have specific advantages in terms of simplicity, prediction accuracy, and interpretability. The industrial application of the EEMD-SEN method is discussed in this paper, and a new virtual sample generation method is proposed to address the modeling problem based on small sample spectral data.

Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.

Job start-time delay often occurs in the steelmaking and continuous casting (SCC) production that changes constraints or assumptions on which the initial scheduling plan is based. The development of effective rescheduling method that allows the system to promptly react to such disruption becomes essential to improve productivity, reduce production costs, and enable efficient material and energy utilization. This paper tackles this challenge and presents a comprehensive analysis of start-time delay disturbance, its consequences, and strategies to resolve conflicts. A heuristic rescheduling algorithm is then proposed to allow the system to remain alert to this type of disruption in real SCC production, and to quickly react it with an optimal rescheduling plan that has the minimum total waiting time. The proposed methodology and algorithm are applied to and illustrated through both a simulated prototypical SCC system and Shanghai BaoSteel plant, a real industrial setting.

The steelmaking-continuous-casting (SCC) process, which includes steelmaking, refining and continuous casting, is one of the major bottlenecks of iron and steel production. Efficient and effective scheduling of this process is essential to improve the productivity and reduce the production costs of the entire production system. We present a time-index formulation for this scheduling problem and a Lagrangian relaxation (LR) approach based on the relaxation of the machine capacity constraints. The relaxed problem is solved using an efficient polynomial dynamic programming algorithm. The corresponding Lagrangian dual (LD) problem is solved using a deflected conditional subgradient level method. Unlike the conventional subgradient algorithms for the LD problem, our method guarantees convergence using the Brannlund's level control strategy to replace the strict convergence condition that the optimum of the dual problem is known a priori. Furthermore, our method enhances the efficiency by introducing a deflected conditional subgradient to weaken the zigzagging phenomena that slows the convergence of conventional subgradient algorithms. The computational results demonstrate that the approaches can quickly obtain high-quality solutions and are notably promising for the SCC scheduling. Note to Practitioners-Efficient and effective SCC schedule is vital for the manufacturing system of iron and steel production. Unfortunately, the scheduling is extremely difficult because of its combinatorial nature and practical complex constraints such as job grouping constraints, precedence constraints, different transport time, and setup times. To obtain high-quality solutions within an acceptable computational time, we can use a problem-oriented approach, which can be the LR. However, there are two deficiencies in this approach: its empirical termination criteria, such as maximal iteration number or running time, which make it difficult to find a golden rule for various problems, and the inefficiency, which is caused by the so-called zigzagging phenomena. To overcome these deficiencies, this paper develops an effective subgradient method for SCC scheduling based on the machine capacity relaxation. This method gives an objective termination criterion based on the convergence condition of the method, and improves the efficiency based on a new search direction or a new subgradient. Then, the work shows how this method can be applied to solve an SCC scheduling problem. The computational results confirm their effectiveness and efficiency. The approaches can also be applied to other similar production scheduling problems.

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Clinker free lime (f-CaO) content plays a crucial role in determining the quality of cement. However, the existing methods are mainly based on laboratory analysis and with significant time delays, which makes the closed-loop control of f-CaO content impossible. In this paper, a multisource data ensemble learning-based soft sensor model is developed for online estimation of clinker f-CaO content. To build such a soft sensor model, input flame images, process variables, and the corresponding output f-CaO content data for a rotary cement kiln were collected from No. 2 rotary kiln at Jiuganghongda Cement Plant which produces 2000 tonnes of clinker per day. The raw data were preprocessed to distinguish the flame image regions of interest (ROI) and remove process variable outliers. Three types of flame image ROI features, i.e., color, global configuration, and local configuration features, were then extracted without segmentation. Further, a kernel partial least square technique was applied for extracting the compressed score matrix features from the concatenated flame image features and filtered process variables to avoid high-dimensional, nonlinear, and correlated problems. Feed-forward neural networks with random weights were employed as base learners in our proposed ensemble modeling framework, which aims to enhance the model's reliability and prediction performance. A total of 157 flame images, the associated process variable data, and the experimentally measured f-CaO content data were used in our experiments. A comparative study on the f-CaO content estimator built by various feature compressed techniques and learner models and robustness analysis were carried out. The results indicate that the proposed multisource data ensemble soft sensor model performs favorably and has good potential in real world applications.

In this paper, a novel pseudo gradient based DE approach is proposed, which takes advantage of both the differential evolutionary (DE) and the gradient-based algorithm. The gradient information, which is called pseudo gradient, is generated through randomly selected two vectors and their fitness function values. This work is to investigate the effect of proposed pseudo gradient on differential evolutionary algorithm. The simulation results show that DE with pseudo gradient can obtain better performance overall in comparison with classical DE variants. The pseudo gradient based DE with adaptive parameter section is compared with the existing adaptive DE algorithms. Also, the control parameter, step size are investigated to understand the mechanism of pseudo gradient in detail.

The whole production line of hematite ore processing is composed of raw ore processing, shaft furnace roasting, grindings, and magnetic separation production phases. Their automation systems consist of the process control part and the operational optimization system. The target of the optimal operational control is to optimize the concerned operational indices, namely, the intermediate product quality, efficiency, and consumptions. The dynamics between the operational indices and the global production indices (i.e., the total concentration grade, metal recovery rate, production rate, beneficiation ratio, and costs) with month, day, and hour time scales changes in line with the variations of production conditions, composition of raw ore together with capability of equipment. These indices are difficult to measure online and as a result it is difficult to model accurately. Moreover, there are characteristics in terms of both interconnections and conflictions among these indices. This leads to isolated operation of individual automation systems for these processes and the optimization of global production indices for whole production line cannot be realized. This paper presents a novel problem description for the integrated optimization of the automation systems of mineral processing. For this purpose, the analysis is made on the difficulty of using the existing optimization methods-based decision making methods to obtain the integrated optimization of the automation systems. The integrated optimization strategy for the automation systems of mineral processes is proposed using our previously established target value optimization of global production indices , two time scales decomposition approach and target value optimization of operational indices. The proposed strategy aims at realizing the optimization of global production indices. Using real data from a mineral processing plant on hematite beneficiation process, relevant simulations, and real industrial experiments have been carried out. The obtained experimental results show the efficiency and effectiveness of the proposed strategy.

The 17 papers in this special section focus on integrated optimization of industrial automation.

For large electricity users, such as smelting plants, their electric loads cannot exceed a concerted limit in production. Traditional single-furnace optimization methods aim to satisfy the electric demand of a furnace to improve its production, and hence cannot consider the maximum demand constraint in a smelting plant. Maximum demand (MD) control is often utilized to keep the total electric demand within the limit via shedding the electric loads of some furnaces once the demand approaches the limit. However, the control method will enlarge the fluctuation of electric loads, which does harm to the production and causes a decline in energy-efficiency. In this paper, we propose a multifurnace optimization strategy to improve the production targets of a whole plant instead of a single furnace. In the strategy, an offline multiobjective load scheduling is first performed to assign electric loads for furnaces in each sampling period, taking into account of the MD constraint and production constraints. A multiobjective particle swarm optimization algorithm, combined with population initialization and constraint-handing strategies, is proposed to search for the Pareto optimal set of the scheduling problem, from which decision-makers can select one solution as the load scheduling program. A double closed-loop control mechanism is used to change the scheduled load into detailed load setpoints of furnaces and keep the actual loads up with the load setpoints. In the outer loop, the detailed load setpoints of furnaces are dynamically adjusted based on the deviation of actual loads from the scheduled loads. Thereafter, the desired setpoints are sent to the automatic control mechanism of each furnace, which is in the inner loop and responsible to keep the actual load up with the setpoint via a proportional-integral-derivative (PID) controller. The case study on a typical magnesia-smelting plant shows that the proposed multifurnace optimization strategy can achieve an increase of about 12.29% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight reduction of 2.35% in electricity cost over the results of MD control. Note to Practitioners - For large electricity users, such as smelting plants, they are subjected to the maximum electric demand constraint. The maximum demand control device is widely adopted to solve the problem, but it will cause a decline in production output and energy-efficiency. This paper was motivated by improving the multiple production targets (i.e, the total production output, the product quality, and the total electricity cost) of a plant via load scheduling and control. In contrast to the maximum demand control, the load scheduling and control approach is a beforehand strategy that can optimize the operation. A case study on a magnesia-smelting plant shows that the proposed approach performs better than the maximum demand control technique.

Compensation of hysteresis nonlinearities in smart material based actuators presents a challenging task for their applications. Many approaches have been proposed in the literature, including the inverse multiplicative scheme. The advantage for such a scheme is to avoid direct model inversions. However, the approach is mainly developed for the Bouc-Wen model. Focusing on the Preisach model which is utilized to describe magnetostrictive actuators, in this paper an inverse compensation approach for Preisach model using the inverse multiplicative structure is developed. Since the input signal is implicitly involved in the Preisach model, it imposes a great challenge to construct the inverse function of the model. To obtain an explicit expression of the input signal from its implicit form so that the inverse multiplicative technique can be applied, the Preisach model is decomposed into a non-memory part and memory part. Using this separation, it only requires to solve the inverse of the non-memory part to obtain an explicit expression of the input signal, thus avoiding constructing the inverse for entire complex dual integral formulation of the Preisach model. Experimental results for a magnetostrictive actuator demonstrate the effectiveness of the proposed approach.

This paper introduces the development and implementation of a ball mill grinding circuit simulator, NEUSimMill. Compared to the existing simulators in this field which focus on process flowsheeting, NEUSimMill is designed to be used for the test and verification of grinding process control system including advanced control system such as integrated control. The simulator implements the dynamic ball mill grinding model which formulates the dynamic responses of the process variables and the product particle size distribution to disturbances and control behaviors as well. First principles models have been used in conjunction with heuristic inference tools such as fuzzy logic and artificial neural networks: giving rise to a hybrid intelligent model which is valid across a large operating range. The model building in the simulator adopts a novel modular-based approach which is made possible by the dynamic sequential solving approach. The simulator can be initiated with connection to a real controller to track the plant state and display in real-time the effect of various changes on the simulated plant. The simulation model and its implementation is verified and validated through a case of application to the design, development, and deployment of optimal setting control system.

Efficient operation of the integrated optimization or automation system in an industrial plant depends mainly on good measurement of product quality. However, measuring or estimating the product quality online in many industrial plants is usually not feasible using the available techniques. In this paper, a data-driven soft-sensor using case-based reasoning (CBR) and fuzzy-similarity rough sets is proposed for product quality estimation. Owning to the sustained learning ability, the modeling of a CBR soft-sensor does not need any additional model correction which is otherwise required by the neural network based methods to overcome the slow time-varying nature of industrial processes. Because the conventional k-nearest neighbor ( k-NN) algorithm is strongly influenced by the value of k, an improved k-NN algorithm with dynamic adjustment of case similarity threshold is proposed to retrieve sufficient matching cases for making a correct estimation. Moreover, considering that the estimation accuracy of the CBR soft-sensor system is closely related to the weights of case feature, a feature weighting algorithm using fuzzy-similarity rough sets is proposed in this paper. This feature weighting method does not require any transcendental knowledge, and its computation complexity is only linear with respect to the number of cases and attributes. The developed soft-sensor system has been successfully applied in a large grinding plant in China. And the application results show that the system has achieved satisfactory estimation accuracy and adaptation ability.

In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectively avoids the need to use the unknown control input directly, and enables the estimation of the un-modeled dynamics with a relatively simple algorithm. Moreover, it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un-modeled dynamics. Finally, simulation studies are presented to demonstrate the effectiveness of the proposed method.

Process monitoring and fault diagnosis of the continuous annealing process lines (CAPLs) have been a primary concern in industry. Stable operation of the line is essential to final product quality and continuous processing of the upstream and downstream materials. In this paper, a multilevel principal component analysis (MLPCA)-based fault diagnosis method is proposed to provide meaningful monitoring of the underlying process and help diagnose faults. First, multiblock consensus principal component analysis (CPCA) is extended to MLPCA to model the large scale continuous annealing process. Secondly, a decentralized fault diagnosis approach is designed based on the proposed MLPCA algorithm. Finally, experiment results on an industrial CAPL are obtained to demonstrate the effectiveness of the proposed method.

Due to complex dynamic characteristics of the ball mill system, it is difficult to measure load parameters inside the ball mill. It has been noticed that the traditional single-model and ensemble-model based soft sensor approaches demonstrate weak generalization power. Also, mill motor current, feature subsets of the shell vibration and acoustical frequency spectra contain different useful information. To achieve better solutions and overcome these problems mentioned above, a selective ensemble multisource information approach is proposed in this paper. Only the useful feature subsets of vibration and acoustical frequency spectra are portioned and selected. Some modeling techniques, such as fast Fourier transform (FFT), mutual information (MI), kernel partial least square (KPLS), brand and band (BB), and adaptive weighting fusion (AWF), are combined effectively to model the mill load parameters. The simulation is conducted using real data from a laboratory-scale ball mill. The results show that our proposed approach can effectively fusion the shell vibration, acoustical and mill motor current signals with improved model generalization.

This paper addresses the integrated multiobjective production planning and decomposing (P&D) problem for mineral processing. A novel multiobjective 0-1 mixed integer nonlinear programming model is presented for the simultaneous P&D problem (O-model). In order to reduce the computational cost for solving O-model, a rolling horizon-based two-level decomposition approach is proposed to separate O-model into an upper level model (H-model) and a lower level model (L-model). An interactive partition (IP) and multiobjective gradient (MO-G)-based hybrid evolutionary multiobjective (EMO) algorithm named as IG-NSGA-II/IG-SPEA2, which takes the popular NSGA-II/SPEA2 as the basic EA, is proposed to solve both H-model and L-model, where an IP technique is designed to generate the efficient feasible combinational nodes, an ideal solution technique is provided for fathoming the infeasible nodes, an improved multiobjective gradient-based operator is developed to accelerate the evolution process in each selected node, and a cut with all continuous variables is constructed to exclude the previous feasible combination if it is not desired for the decision makers (DMs). The experimental results demonstrate that the presented two-level decomposition strategy can effectively integrate both levels. Moreover, the proposed hybrid method can effectively reduce the combinatorial space so as to concentrate the computing resource on the subspace of most interest, and can generate better feasible solutions than the pure EA in the full-space under computation time limits.

By representing nonlinear systems as a combination of linear part and unmodeled dynamics, in this paper, an improved estimation algorithm using an adaptive neuro-fuzzy inference system (ANFIS) for unmodeled dynamics is presented. At first, the unmodeled dynamics is divided into two parts using the differential expansion of the control input at the last time instant; then, the two parts are estimated by the ANFIS. It has been shown that the proposed algorithm overcomes the problem that the unknown control input is embedded in unmodeled dynamics, which makes the true value of unmodeled dynamics difficult obtain. Moreover, the method improves the precision of the estimation of unmodeled dynamics. Second, under the assumption that the growth rate of unmodeled dynamics does not exceed its input vector, the “one-to-one mapping” and “regularization technique” are adopted to deal with the input and output data and the unmodeled dynamics, respectively. As a result, the data vector can be guaranteed to lie inside a compact set, which ensures the use of the universal approximation property of the ANFIS. On the other hand, it has been shown that datum of a system can be fully used to obtain the parameters (centers, widths) in membership functions and the network connection weights in the ANFIS by offline training. These parameters are tuned online to improve the estimation convergence rate of the unmodeled dynamics. The effectiveness of the proposed estimation method is illustrated by comparing it with the simulation results that are obtained from the other existing methods. Finally, the proposed estimation method is applied to the nonlinear switching control design. Both simulation and theoretical analysis have confirmed that the nonlinear switching control which adopts the proposed estimation method cannot only guarantee the stability and convergence of the system but can exhibit a desired dynamic performance for the closed-loop system as well.

Complex industrial processes are controlled by the local regulation controllers at the field level, and the setpoints for the regulation are usually made by manual decomposition of the overall economic objective according to the operators' experience. If a precise static process model can be built, real-time optimization (RTO) can be used to generate the setpoints. Nevertheless, since the aforementioned control structure is actually open-loop, the desired economic objective of the whole processes may not be tracked when disturbances exist. Aiming at solving this problem, a novel network based model predictive control method (MPC) for setpoints compensation is proposed in this paper. Firstly, a multivariable proportional integral (PI) controller is designed to perform the local regulation control. Secondly, a stochastic packet dropout model is adopted to characterize the measurement and human-in-the-loop delay effect. Then, a model predictive controller considering the random dropout effect is developed to compensate the setpoints dynamically according to the changing conditions of the processes, such that the prescribed performance objective can be obtained. Finally, a flotation process model is employed to demonstrate the effectiveness of the proposed method.

Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.

In this paper, a novel knowledge-based global operation approach is proposed to minimize the effect on the production performance caused by unexpected variations in the operation of a mineral processing plant subjected to uncertainties. For this purpose, a feedback compensation and adaptation signal discovered from process operational data is employed to construct a closed-loop dynamic operation strategy. It uses the signal to regulate the outputs of the existing open-loop and steady-state based system so as to compensate the uncertainty in the steady-state operation at the plant-wide level. The utilization mechanism of operational data through constructing increment association rules is firstly described. Then, a rough set based rule extraction approach is developed to generate the compensation rules. This includes two steps, namely the determination of the variables to be compensated based on the significance of attributes in the rough set theory and the extraction of the compensation rules from process data. Based upon the operational data of the mineral processing plant, relevant rules are obtained. Both simulation and industrial experiments are carried out for the proposed global operation, where the effectiveness of the proposed approach has been clearly justified.

Accurate and robust recognition of burning state for sintering process of rotary kiln plays an important role in the design of image-based intelligent control systems. Existing approaches such as consensus-based methods, temperature-based methods and image segmentation-based methods could not achieve satisfactory performance. This paper presents a flame image-based burning state recognition system using a set of heterogeneous features and fusion techniques. These features, i.e., the color feature, the global and local configuration features, are able to characterize different aspects of flame images, and they can be extracted from pixel values directly without segmentation efforts. In this study, ensemble learner models with four types of base classifiers and five fusion operators are examined with comprehensive comparisons. A total of 482 typical flame images, including 86 over-burning state images, 193 under-burning state images, and 203 normal-burning state images, were used in our experiments. These images were collected from the No. 3 rotary kiln at the Shanxi Aluminum Corporation in China, and labeled by the rotary kiln operational experts. Results demonstrate that our proposed image-based burning state recognition systems outperform other methods in terms of both recognition accuracy and robustness against the disturbance from smoke and dust inside the kiln.

In this paper, a new online monitoring approach is proposed for handling the dynamic problem in industrial batch processes. Compared to conventional methods, its contributions are as follows: (1) multimodes are separated correctly since the cross-mode correlations are considered and the common information is extracted; (2) the expensive computing load is avoided since only the specific information is calculated when a mode is monitored online; and (3) after that, two different subspaces are separated, and the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the subspace. The corresponding confidence regions are constructed according to their respective models.

Component concentration of sodium aluminate solution is an important quality index for alumina production. In this article, we propose a new on-line soft sensing strategy for measuring component concentration of sodium aluminate solution. With this method, on-line control can be realised in aluminate production plants. Several advance techniques are used, such as principal component analysis (PCA), neural modelling and the least square algorithm. Industry experiments are conducted in the alumina production process and the results show the effectiveness of this method.

The production planning optimization for mineral processing is important for non-renewable raw mineral resource utilization. This paper presents a nonlinear multiobjective programming model for a mineral processing production planning (MPPP) for optimizing five production indices, including its iron concentrate output, the concentrate grade, the concentration ratio, the metal recovery, and the production cost. A gradient-based hybrid operator is proposed in two evolutionary algorithms named the gradient-based NSGA-II (G-NSGA-II) and the gradient-based SPEA2 (G-SPEA2) for MPPP optimization. The gradient-based operator of the proposed hybrid operator is normalized as a strictly convex cone combination of negative gradient direction of each objective, and is provided to move each selected point along some descent direction of the objective functions to the Pareto front, so as to reduce the invalid trial times of crossover and mutation. Two theorems are established to reveal a descent direction for the improvement of all objective functions. Experiments on standard test problems, namely ZDT 1-3, CONSTR, SRN, and TNK, have demonstrated that the proposed algorithms can improve the chance of minimizing all objectives compared to pure evolutionary algorithms in solving the multiobjective optimization problems with differentiable objective functions under short running time limitation. Computational experiments in MPPP application case have indicated that the proposed algorithms can achieve better production indices than those of NSGA-II, T-NSGA-FD, T-NSGA-SP, and SPEA2 in the case of small number of generations. Also, those experimental results show that the proposed hybrid operators have better performance than that of pure gradient-based operators in attaining either a broad distribution or maintaining much diversity of obtained non-dominated solutions.

Modeling of friction forces has been a challenging task in mechanical engineering. Parameterized approaches for modeling friction find it difficult to achieve satisfactory performance due to the presence of nonlinearity and uncertainties in dynamical systems. This paper aims to develop adaptive fuzzy friction models by the use of data-mining techniques and system theory. Our main technical contributions are twofold: extraction of fuzzy rules and formulation of a static fuzzy friction model and adaptation of the fuzzy friction model by the use of the Lyapunov stability theory, which is associated with a control compensation of a typical motion dynamics. The proposed framework in this paper shows a successful application of adaptive data-mining techniques in engineering. A single-degree-of-freedom mechanical system is employed as an experimental model in simulation studies. Results demonstrate that our proposed fuzzy friction model has promise in the design of uncertain mechanical control systems.

The 21 papers in this special section focus on data-based control, modeling, and optimization.

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

This paper presents a novel nonlinear control strategy for a class of uncertain single-input and single-output discrete-time nonlinear systems with unstable zero-dynamics. The proposed method combines adaptive-network-based fuzzy inference system (ANFIS) with multiple models, where a linear robust controller, an ANFIS-based nonlinear controller and a switching mechanism are integrated using multiple models technique. It has been shown that the linear controller can ensure the boundedness of the input and output signals and the nonlinear controller can improve the dynamic performance of the closed loop system. Moreover, it has also been shown that the use of the switching mechanism can simultaneously guarantee the closed loop stability and improve its performance. As a result, the controller has the following three outstanding features compared with existing control strategies. First, this method relaxes the assumption of commonly-used uniform boundedness on the unmodeled dynamics and thus enhances its applicability. Second, since ANFIS is used to estimate and compensate the effect caused by the unmodeled dynamics, the convergence rate of neural network learning has been increased. Third, a "one-to-one mapping" technique is adapted to guarantee the universal approximation property of ANFIS. The proposed controller is applied to a numerical example and a pulverizing process of an alumina sintering system, respectively, where its effectiveness has been justified.

Texture feature has been widely used in image segmentation, classification, retrieval and many others. Among various approaches to texture feature extraction, Gabor filtering has emerged as one of the most popular in recent years. Gabor filter-based texture feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smoothing parameters of the Gaussian envelope. In the literature, these parameters are often set by trial and error, based on the experience of the user, and the Gabor filter banks thus designed are often over-sized. To address the problem mentioned above, we propose to design compact Gabor filter banks by incorporating filter selection in this study. We develop a new Mahalanobis separability measure-based supervised approach to address the need of texture feature extraction. The strengths of our methods are twofold. Firstly, the proposed method provides a systematic way for Gabor filter bank design to avoid man-made bias. Secondly, the compact filter banks thus designed overcomes the problem of redundant or insignificant/irrelevant filter banks, and this in turn leads to improved performance of texture classification. Experimental results on benchmark datasets demonstrate the effectiveness of our proposed approach.

Texture feature has been widely used in object recognition, image content analysis and many others. Among various approaches to texture feature extraction, Gabor filter has emerged as one of the most popular ones. Gabor filter-based feature extractor is in fact a Gabor filter bank defined by its parameters including frequencies, orientations and smooth parameters of Gaussian envelope. In the literature, different parameter settings have been suggested, and filter banks created by these parameter settings work well in general. From the perspective of pattern classification, however, filter banks thus designed may not be ideal. In the present study, we propose a new approach to Gabor filter bank design, by incorporating feature selection, i.e. filter selection, into the design process. The merits of incorporating filter selection in filter bank design are twofold. Firstly, filter selection produces a compact Gabor filter bank and hence reduces computational complexity of texture feature extraction. Secondly, Gabor filter bank thus designed produces low-dimensional feature representation with improved sample-to-feature ratio, and this in turn leads to improved performance of texture classification. Experiment results on benchmark datasets and a real application have demonstrated the effectiveness of the proposed method.

Cascade process, such as wastewater treatment plant, includes many nonlinear sub-systems and many variables. When the number of sub-systems is big, the input-output relation in the first block and the last block cannot represent the whole process. In this paper we use two techniques to overcome the above problem. Firstly we propose a new neural model: hierarchical neural networks to identify the cascade process; then we use serial structural mechanism model based on the physical equations to connect with neural model. A stable learning algorithm and theoretical analysis are given. Finally, this method is used to model a wastewater treatment plant. Real operational data of wastewater treatment plant is applied to illustrate the modeling approach.

In this paper, an adaptive generalized predictive control method using adaptive-network-based fuzzy-inference system (ANFIS) and multiple models is proposed for a class of uncertain discrete-time nonlinear systems with unstable zero-dynamics. The proposed controller consists of a linear and robust generalized predictive adaptive controller, a nonlinear generalized predictive adaptive controller based on ANFIS, and a switching mechanism. It has been shown that the linear generalized predictive adaptive controller can ensure the boundedness of the input and output signals, and the nonlinear generalized predictive controller can improve the transient performance of the system. By switching between the two earlier described controllers, the switching mechanism can simultaneously improve the performance and ensure the closed-loop stability. Moreover, the method has relaxed the global boundedness assumption of the higher order nonlinear term and established the analysis of stability and convergence of the closed-loop system. In the proposed controller, ANFIS is adopted to estimate and compensate the unmodeled dynamics, which avoids some possible flaws of a backpropagation (BP) neural network. Simulation results have demonstrated the superiority of the proposed method and verified the theoretical analysis.

In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.

During the operation of a grinding circuit (GC) in mineral processing plant the main purpose of control and optimal operation is to control the product quality index, namely the product particle size, into its technically desired ranges. Moreover, the grinding production rate needs to be maximized. However, due to the complex dynamic characteristics between the above two indices and the control loops, such control objectives are difficult to achieve using existing control methods. The complexity is reflected by the existence of process heavy nonlinearities, strong coupling and large time variations. As a result, the lower level loop control with human supervision is still widely used in practice. However, since the setpoints to the involved control loops cannot be accurately adjusted under the variations of the boundary conditions, the manual setpoints control cannot ensure that the actual production indices meet with technical requirements all the time. In this paper, an intelligent optimal-setting control (IOSC) approach is developed for a typical two-stage GC so as to optimize the production indices by auto-adjusting on line the setpoints of the control loops in response to the changes in boundary conditions. This IOSC approach integrates case-based reasoning (CBR) pre-setting controlling, neural network (NN)-based soft-sensor and fuzzy adjusting into one efficient control model. Although each control element is well known, their innovative combination can generate better and more reliable performance. Both industrial experiments and applications show the validity and effectiveness of the proposed IOSC approach and its bright application foreground in industrial processes with similar features.

The notion of a rough set was originally proposed by Pawlak underwent a number of extensions and generalizations. Dubois and Prade (1990) introduced fuzzy rough sets which involve the use of rough sets and fuzzy sets within a single framework. Radzikowska and Kerre (2002) proposed a broad family of fuzzy rough sets, referred to as ( t)-fuzzy rough sets which are determined by some implication operator (implicator), and a certain t-norm. In order to describe the linguistically represented concepts coming from data available in some information system, the concept of fuzzy rough sets are redefined and further studied in the setting of the Axiomatic Fuzzy Set (AFS) theory. Compared with the ( t)-fuzzy rough sets, the advantages of AFS fuzzy rough sets are twofold. They can be directly applied to data analysis present in any information system without resorting to the details concerning the choice of the implication, t-norm and a similarity relation S. Furthermore such rough approximations of fuzzy concepts come with a well-defined semantics and therefore offer a sound interpretation. Some examples are included to illustrate the effectiveness of the proposed construct. It is shown that the AFS fuzzy rough sets provide a far higher flexibility and effectiveness in comparison with rough sets and some of their generalizations.

In this paper, a new method for adaptive control of general nonlinear and non-Gaussian unknown stochastic systems has been proposed. The method applies the minimum entropy control scheme to decrease the closed-loop randomness of the output under an iterative learning control (ILC) basis. Both modeling and control of the plant are performed using dynamic neural networks. For this purpose, the whole control horizon is divided into a certain number of time domain subintervals called batches and a pseudo-D-type ILC law is employed to train the plant model and controller parameters so that the entropy of the closed-loop tracking error is made to decrease batch by batch. The method has the advantage of decreasing the output uncertainty versus the advances of batches along the time horizon. The analysis on the proposed ILC convergence is made and a set of demonstrable experiment results is also provided to show the effectiveness of the obtained control algorithm, where encouraging results have been obtained.

In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: 1) nonlinear pure-feedback systems and 2) nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in the discrete-time control design, both systems are transformed to a predictor for output feedback control design. Implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input. The problem of lacking a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order neural network (HONN) is employed to approximate the unknown control. The closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to demonstrate the effectiveness of the proposed control.

In this letter, a nonlinear adaptive dynamical decoupling control algorithm using neural networks (NNs), a novel technique, is proposed for a class of uncertain nonlinear multivariable discrete-time dynamical systems. By combining open-loop decoupling compensation and generalized minimum variance adaptive scheme with NNs, complete dynamical decoupling is realized. The algorithm is applicable to the systems which are open-loop unstable and nonminimum phase in a neighborhood of the origin [symbol: see text]. In the domain [symbol: see text], it can assure the bounded-input-bounded-output (BIBO) stability of the closed-loop system and can also make the generalized tracking error converge to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the NN. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

This paper develops a guaranteed cost networked control (GCNC) method for Takagi–Sugeno (T–S) fuzzy systems with time delays. The state feedback controller is designed via the networked control system (NCS) theory. The stability of the overall fuzzy system using GCNC is also established. Network-induced delay in network transmission and packet dropout are analyzed. Some deductions are also extended to uncertain systems. Simulation results show the validity of the present control scheme.

In this correspondence, we show that the relationship between the finite optimal trajectory and the infinite optimal trajectory in Lemma 3 of the aforementioned paper cannot act as a theory basis for the further implementation of other theorems and the dynamic decomposition algorithm.

In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand.

Since the contemporary integrated manufacturing system (CIMS) which is designed according to the conventional five layer Purdue model architecture separates control process and management process virtually, it leads to high production cost and low ore recovery ratio for the complicated process of ore dressing, this paper presents a systematic approach to develop the CIMS based on the three-layer enterprise resource planning (ERP)/manufacturing executive system (MES)/process control system (PCS) architecture for the ore dressing plant. The effective and efficient real time supervision techniques of production statistics & analysis, material current, production cost, equipments and quality are proposed to integrate control process and management process. The successful implementation of ore dressing plant CIMS of JiuQuan steel Company in China confirms the viability and effectiveness of the approach.

In this paper, an observer-based robust adaptive fuzzy tracking control for rigid robotic systems is presented with plant unknown. It is assumed that only the joint angular positions are measured, the joint angular velocities are estimated via a fuzzy observer. First, we design a nonlinear observer based on fuzzy basis functions (FBF) to estimate the joint angular velocities in which a fuzzy logic system is introduced to learn these unknown dynamics by an adaptive algorithm. Then, an indirect adaptive fuzzy controller based on observer is presented. The developed control scheme is simple and computationally efficient, since it does not require a knowledge of either the mathematical model or the parameterization of the robotic dynamics. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

This paper presents an application of an adaptive fuzzy system for compensating the effects induced by the friction in mechanical system. An adaptive fuzzy system based on fuzzy basis functions is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The hybrid-controller is a combination of a PD controller and an adaptive fuzzy controller which compensates for nonlinear friction. The proposed scheme is implemented and tested on a DC motor control system. The algorithm and simulations results are described. The results are relevant for many precision drives, such as those found in robot.

In this paper, a high order open and closed ILC (iterative learning control) scheme with initial state learning is presented. The convergent bounds are only dependent on the system uncertainties and disturbances but independent of the initialization errors. The scheme performs better than common ILC scheme with initial state learning both in convergence rate and transient performance. By adding closed loop, the whole algorithm has better performance in both stability and convergence than the open loop one alone. Furthermore, the effectiveness of the proposed method is illustrated by simulation experiments.

This paper presents an iterative learning scheme for a linear time-invariance (LTI) system. Based on the scheme, the influence of learning structure, learning laws and learning parameters on the convergence speed of iterative learning control (ILC) is discussed. Furthermore, the theoretical proof and simulation conclusions are provided, which is the theoretical instruction and designing guideline for practical applications of ILC.

This paper presents the new development of the boiler-turbine coordinated control system using fuzzy reasoning and auto-tuning techniques. The boiler-turbine system is a very complex process which is a multivariable, nonlinear, slowly time-varying plant with large settling time and a lot of uncertainties. A special subclass of fuzzy inference systems, called the GPE(Gaussian partition with evenly spaced midpoints) systems, is used to self-tune the main steam pressure PID controller's parameters on-line based on the error signal and its first difference, aimed at overcoming the uncertainties due to changing fuel calorific value, machine wear, contamination of the boiler heating surfaces and plant modeling errors. For the large variation of operating condition, a supervisory control level has been developed by auto-tuning technique. Satisfactory industrial application results show that such a control system has enhanced adaptability and robustness to the complex process, and better control performance and high economic benefit has been obtained.

In this paper, a stable fuzzy direct control scheme is presented for a class of interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In this control algorithm, fuzzy logic systems are employed to approximate the optimal controllers, which are designed on the assumption that all dynamics for each subsystem are known; then the fuzzy controllers and adaptation mechanisms for each subsystem depend only on local measurements to provide asymptotic tracking of a reference trajectory. In addition, a fuzzy sliding mode controller is developed to compensate for the fuzzy approximating errors and attenuate the interactions between subsystems. Global asymptotic stability is established in the Lyapunov sense, with the tracking errors converging to a neighborhood of zero. A simulation example is given to illustrate the performance of the proposed method.