Stimulated by growing requirements for security and reliability in Cyber-Physical Systems (CPS), this paper proposes a federated intrusion detection framework integrating improved prototype learning and adjusted-logits Cross-Entropy loss to address the challenges of hybrid feature-label shifts. A dual-path federated learning approach (DP-FL) with prototype alignment and dynamic logits is proposed, featuring a two-branch architecture: At first, a dynamically log-weighted prototype aggregation mechanism employing dual adaptive factors is introduced, achieving a more balanced and informative global prototype. Building upon this, a dynamic logits adjustment mechanism is further designed to calibrate the decision boundaries of local client models by jointly considering both label frequency and prototype divergence, thereby strengthening the discriminative capability and generalization efficacy of the model. Finally, the validation of the proposed DP-FL framework’s effectiveness is conducted on the NSL-KDD and CICIDS2017 datasets. Experimental results show that the proposed DP-FL framework outperforms existing methods under scenarios involving hybrid feature-label distribution shifts.

Against the backdrop of Industry 4.0, complex industrial processes face heightened risks of performance degradation and safety hazards due to increasing scale and integration. To address this, we propose a novel cloud-edge-device collaborative framework for hierarchical performance monitoring and fault diagnosis. Our approach explicitly targets multi-performance coupling and cross-domain coordination in complex systems. Aligned with the architecture of manufacturing systems, a cloud-edge-device collaborative framework is designed deeply integrating models for safety control, quality monitoring, and energy consumption prediction. The architecture strategically distributes tasks: (1) At the device layer, real-time control-loop safety is considered and the safety-related data processing and alignment are realized; (2) The edge layer deploys a dual-attention minimal gated unit network coupled with a quality-driven autoencoder, enabling temporal and nonlinear feature extraction for quality-centric fault diagnosis within sub-processes; (3) The cloud layer integrates self-attention minimal gated units with a broad learning system for multi-perspective energy efficiency prediction and holistic evaluation. Finally, a hot strip mill process prototype system is designed and developed to validate the effectiveness and engineering value of the proposed framework through practical case studies.

Fault diagnosis plays a critical role in ensuring industrial safety, production efficiency, and product quality. In practical industrial processes, various process variables exhibit distinct sensitivities to different fault types. Traditional methods that directly model and analyze multivariate process data tend to introduce redundant information and ignore the dynamic interdependencies among variables, thereby deviating the fault diagnosis mechanism from actual industrial operating laws. Such issues inevitably impair the model interpretability and degrade the overall fault diagnosis performance. Therefore, to effectively utilize the multivariate information of industrial processes and enhance the model interpretability and fault diagnosis performance, in this paper, a dynamic dependencies of fault-related variables based interpretable fault diagnosis framework is proposed. Specifically, all process variables are screened based on the distribution differences between normal and fault samples for selecting fault-related variables. Subsequently, a new graph structure learning module is constructed to capture the dynamic dependencies among fault-related variables and build a graph, which is incorporated into feature enhancement and fusion process of fault-related variables to improve the representation capability of fault features. After that, fault diagnosis is performed based on the fused features, improving the diagnosis accuracy and interpretability. Finally, three case studies based on the hot rolling process are designed to validate the effectiveness, feasibility and generalization performance. Experimental results show that the proposed method achieves fault diagnosis accuracies of 97.67%, 95.5%, and 98.12% in three cases, respectively.

As large-scale industrial processes evolve toward greater complexity, the increasing interdependence of networked and dynamic process data has a critical impact on product quality, creating significant challenges for quality-related fault diagnosis. Causal graphs (CGs) are effective in modeling structural relationships among nodes in large-scale industrial processes. However, traditional causal discovery methods are limited in their ability to represent hierarchical and dynamic causal structures with spatiotemporal features. To overcome these limitations, a dynamic causal entropy (DCE)-spatiotemporal convolutional network is designed in this article. First, the proposed DCE method enables the construction of hierarchical dynamic CGs that accurately represent dynamic interactions among process variables, effectively mitigating confounding factors and enhancing interpretability. Second, a 3-D squeeze-and-excitation (SE) convolutional neural network is designed to adaptively recalibrate channel-wise information and deeply analyze the spatiotemporal characteristics embedded in the hierarchical dynamic CGs. Furthermore, a local-global quality-related fault detection approach is introduced, along with a novel causal anomaly vector that facilitates precise recognition of fault root causes across multiple hierarchical levels. Finally, the effectiveness and practical advantages of the proposed method are thoroughly demonstrated using both numerical simulations and real-world data from a hot strip mill process (HSMP), achieving a fault detection accuracy of 95.78%.

In the increasingly integrated and complex industrial processes, it is difficult for a single type of data to adequately characterize faults due to the correlation and multi-source heterogeneity of process data. Therefore, to achieve accurate fault diagnosis, multi-source heterogeneous data is necessary to be integrated to obtain comprehensive and multi-scale fault information. However, significant differences in dimensions and structures are exhibited by multi-source heterogeneous data, which may make the high-quality data fusion difficult to achieve. Responding to the above issues, in order to achieve a unified representation and fusion of multi-source heterogeneous data, and enhance the fault diagnosis performance, a unified representation and fusion framework is proposed for multi-source heterogeneous data based fault diagnosis in industrial processes. Specifically, the optimal region containing useful information in the fault image data is first selected as the representative region. Subsequently, tensor fusion is performed on the time series data and the selected region of image data respectively, and the low-rank decomposition is used to obtain the low-dimensional unified vector representation for data fusion. Furthermore, a fault diagnosis module based on the multi-scale lightweight convolutional neural network is constructed in which the multi-scale features are extracted and fused from the fusion vector and then used for fault diagnosis. Finally, to validate the effectiveness and superiority of the proposed framework, comparative experiments based on actual data from the hot rolling process are conducted.

Modern industrial plants generally demonstrate the characteristics of large scale, long process, and multiunit collaborative operation, which makes the spatio-temporal distribution an inherent nature, and the quality stability is usually hard to be guaranteed. A quality-related spatio-temporal information analytics based multiunit synergetic monitoring framework is presented in this paper. In this framework, the spatio-temporal properties are analyzed from the unit level and the process level, respectively. Firstly, for each operation unit, the quality supervised spatio-temporal support region is constructed with a concurrent feature extraction strategy. In this strategy, temporal dynamic features are extracted by a long short term memory (LSTM) network with attention mechanism. Concurrently, the spatial feature is extracted with the mutual information-kernel principal component analysis approach. Secondly, for the plant-wide process, a third order multiunit-spatio-temporal feature tensor is constructed for feature fusions. Via tensor decomposition, the interconnected associations among units and the quality inheritance along the process are explored, and the original feature space is decomposed into several subspaces. Finally, a multiunit synergetic monitoring model is developed over subspaces and the comprehensive monitoring results are given by Bayesian fusion. Reasonable interpretations can be provided in the monitoring results. The effectiveness of the proposed framework is verified on a real hot strip mill process.Note to Practitioners—This paper intends to provide a spatio-temporal information analytics and fusion framework for multiunit processes and to develop a quality-related process monitoring method for industrial plants. Different from the existing works, the monitoring model built in this paper is based on the multiunit-spatio-temporal sensitive information, which is extracted by a concurrent strategy and fused by the tensor model. In addition, the quality inheritance among multiunits is considered in this framework and the monitoring results in each subspace can provide helpful instructions for the field technicians. In detail, the fault-relevant unit can be located by the relatively independent subspace monitoring, and the quality-related anomaly propagation tendency can be indicated by the strong associative subspace monitoring.

This article investigates the sliding mode fault-tolerant secure control for cyber-physical system facing jamming attacks and multiple physical faults. An intelligent attacker is capable of emitting interference power and adjusting its strategy by observing the transmitter's sending power, leading to packet dropouts in the controller-to-actuator channel. A Bayesian Stackelberg game is exploited to capture these competitive interactions between the two players, in which the transmitter and the intelligent attacker can only probabilistically obtain information about each other's channel state and transmission cost. Meanwhile, the transmitter has only statistical knowledge about either the presence or absence of the attacker in the practical environment. First, optimal transmission power strategies for both sides are studied using the backward induction method and the Karush-Kuhn-Tucker condition. Second, an integrated observer is designed to simultaneously estimate the system state, actuator fault, and sensor fault. Furthermore, the reaching law is proposed so that the state trajectories can reach the preselect sliding surface during the assigned finite time interval from any initial state. Sufficient criteria are derived to guarantee stochastic finite-time boundedness during reaching and sliding motion phases of closed-loop systems using a sliding-mode fault-tolerant secure controller. Finally, simulation results validate the effectiveness and superiority of the proposed method.

The networked structure of sensors emerges in large-scale industrial processes. Causal graphs can reveal the underlying mechanisms. However, due to the constraints of material and information flows, industrial process data exhibit complex spatio–temporal characteristics. Traditional causal discovery results include redundant information and the spatio–temporal features are not sufficiently mined, affecting the accuracy of fault diagnosis. To address the above problems, an integrated distributed fault diagnosis framework is proposed. First, a new method combining mechanism knowledge and correlation is proposed to construct a spatio–temporal causal graph, which highlight spatio–temporal causal information. Second, an embedded time convolutional network-based autoencoder is designed to extract spatio–temporal features simultaneously. Then, the local-global fault detection scheme is performed. On this basis, a new anomaly status information matrix is designed by decoder and spatial features to achieve root cause recognition. Finally, the effectiveness of the proposed method is validated using actual data from the hot strip mill process, achieving a fault detection accuracy of 98.3$\%$.

In this article, a distributed semi-consensus-based data-driven fault detection scheme is developed based on the process variables collected by sensor networks to ensure the safety of the large-scale dynamic processes. For our purpose, the distributed data-driven process modeling scheme is developed for dynamic systems first by considering the communication topology of the sensor networks. Then, a distributed Kalman filter-based fault detection approach is developed aiming at achieving optimal detection performance at each sensor node. Specifically, the distributed iterative learning algorithm is implemented to calculate the needed parameter matrices for Kalman filter-based residual generator offline with the aid of average consensus algorithm. It is followed by a distributed fusion of local residual signals to perform online optimal fault detection. To avoid the detection delay caused by the traditional average consensus method, the semi-consensus algorithm is developed for the first time to ensure the timely detection of potential faults. A case study on the multiphase flow facility process is given in the end to demonstrate the proposed method.

This paper investigates the finite-time sliding mode fault-tolerant secure control issue for cyber-physical systems under period denial-of-service (DoS) jamming attacks and packet dropouts. The considered cyber attacks are periodic DoS attacks which are modeled by a kind of periodic jamming signals that cause data dropouts on the sensor-to-observer channel. Additionally, an update rule is proposed to characterize packet dropouts caused by intrinsic factors and periodic DoS jamming attacks of wireless channels. Based on the update rule, an observer is presented to obtain the unknown state and fault information. Then, a sliding mode fault-tolerant secure controller is designed using estimated information to ensure the estimated state reaches the sliding surface within a finite time, entering the quasi-sliding mode domain. Moreover, sufficient conditions are provided to guarantee the stochastic finite-time boundedness of the closed-loop system during the whole phase, even in the presence of packet dropouts and DoS jamming attacks. Finally, a simulation result validates the effectiveness and superiority of the proposed method.

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise.Note to Practitioners—Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

The dynamic performance metrics of manipulators are decreased when actuator faults happen. To ensure the response speed and improve the tracking accuracy under actuator fault, the complementary sliding mode (CSM) controller is designed in this article. Different from the general sliding mode control (SMC), there are two sliding surfaces in the CSM controller design process, and the mathematical complexity of the controller is simplified through the relationship of the two complementary sliding surfaces, which helps to reduce the response time. Also, the structural parameter changes and disturbance existed in the real manipulator system are also considered in this paper by introducing uncertainty item during modeling, thus the robustness of the controller is improved. In the experimental part, the control effect of the CSM proposed is compared with that of the conventional sliding mode method. Normal conditions and actuator constant deviation fault of the manipulator are studied. The results show that the CSM has superiority in control accuracy and response speed.Note to Practitioners—In recent years, the control problem of manipulators under complex conditions has attracted particular attention. The key to the design of the controller lies in its stability while keeping the tracking performance. Many complex auxiliary methods are proposed to guarantee this, but a few can achieve acceptable performance. This article proposed the complementary sliding mode controller design method, in which two sliding surfaces are used to simplify the proof in the controller design. We choose the error variables to construct the sliding surface, so the CSM could handle several actuator faults very well, which enhances the adaptive ability of the algorithm.

This article is concerned with the optimal fault detection issues for discrete-time nonlinear systems with the aid of Takagi–Sugeno fuzzy dynamic modeling technique. To this end, in the first part of this article, the nonlinear system is formulated in the time-varying fuzzy manner, and based on it, a unified fault detection approach is developed by solving a multiobjective optimization problem. In this sense, the optimal tradeoff between fault detectability and robustness against unknown inputs is ensured by solving the Riccati equation. Meanwhile, a fuzzy fault detection approach is studied in the second part of this article based on piecewise-fuzzy Lyapunov functions, which is realized by solving linear matrix inequalities. Two examples are given at the end of this article to demonstrate the proposed approaches.

Due to the complex and variable working conditions of manufacturing processes as well as the interconnection and coupling loops, faults of multiple processes, subsystems or control loops may occur at the same time or cascade successively, compound fault becomes the norm. After the compound fault occurs, it may be characterized by the abnormal sensor data, the image or video data. Most of the traditional methods use a single type of sensor data for fault diagnosis, which may affect the diagnosis performance. To solve this problem, considering the heterogeneity of compound fault data, a multitask learning-based collaborative modeling method is proposed for compound fault diagnosis. Specifically, the heterogeneous compound fault data is mapped into a common subspace, of which a dynamic combination coefficient is reasonably set considering the different contributions of unstructured and structured data. Moreover, considering the correlations among heterogeneous data, the low-rank constraint trace norm is introduced into multitask learning, and an attention-based feature fusion network is designed for compound fault diagnosis. Finally, two cases on the actual manufacturing process, hot rolling process, are conducted to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the new collaborative modeling scheme of heterogeneous data can better perform compound fault diagnosis tasks than the existing state-of-the-art algorithms.

Large-scale manufacturing processes are usually made up of multiple interrelated and distributed continuously subprocesses, which are transmitted and connected by information and quality flow. The characteristics of long processes, quality heritability between subprocesses, and dynamic-coupled variables bring severe challenges to conventional quality-related fault diagnosis. Against this background, a novel distributed diagnosis framework for quality-related faults is proposed in this article. First, the sequential manufacturing process is decomposed into multiple subprocesses based on mechanism knowledge. Second, a novel dual-attention quality-driven autoencoder method is designed as the model for local fault diagnosis. Deep nonlinear features are extracted under quality supervision; meanwhile, the dynamic information and the different correlations among variables are also considered. Then, based on the tandem structure of the manufacturing process, multiple dual-attention quality-driven autoencoder models corresponding to each subprocess are constructed and stacked into a distributed model. Bayesian inference is used to build global monitoring statistics. Moreover, after faults occur, the intervariable attention weights are achieved to identify faulty variables. Finally, the effectiveness and advantages of the proposed framework are demonstrated via a practical large-scale sequential manufacturing process, the hot strip mill process.

This article takes into account the problem of adaptive fixed-time control for nonlinear systems in a strict form via finite-time command-filtered backstepping. Our presented control scheme gives consideration to rapidity by using fixed-time control and finite-time command filter, and its prime control objective is to ensure that the system output can be guided from any initial conditions to go after an ideal variable. Meanwhile, this control strategy makes sure that all the system states and other signals are bounded at finite time, and its convergence time does not have any connection with the system initial conditions and is determined by the design parameters. At every step in the design process, a fuzzy logic system is introduced to approximate the unknown part, and a finite-time command filter is also utilized so as to avoid the issue of derivation of virtual control laws and complex calculation. At last, we verify that the presented scheme is viable through a simulation example.

Due to the interconnected characteristics between subsystems and the strong correlation within subsystems, the monitoring of plant-wide processes has become a challenging problem, especially for tandem plant-wide processes that exist in various industrial fields, such as petrochemicals, metallurgy, and sewage treatment. In this article, a novel spatio-temporal monitoring method is proposed for the hot strip mill (HSM) process, a typical tandem industrial process. First, the plant-wide process is divided into different subblocks based on the tandem structure. Then, a distributed conditional variational recurrent autoencoder-based process monitoring method is proposed to build the local latent variable model of each subsystem using relevant dynamic features extracted from the previous subsystem. The latent distributions and reconstructed errors are used to design local monitoring statistics for local process monitoring. A global monitoring statistic is established by deep support vector data description to monitor the whole process. Finally, the effectiveness and superiority of the proposed method are demonstrated by a HSM process case, which shows better monitoring performance compared to the existing methods.

In industrial processes, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is considered as a multi-class classification problem. As a well-known one-class classifier, support vector data description (SVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, SVDD gives the target data set a spherically shaped description, which is a binary output. However, practical industrial data of different operating performance grade may have overlapping region, which is a knotty problem for classification. To handle above issues, a distributed operating performance assessment method based on probabilistic support tensor data description (PSTDD) is proposed in this work. First, the plant-wide process variables are selected and divided into several blocks. Then, a PSTDD model is developed in each block. Based on the assessment results of different blocks, a global assessment index is designed. If the process is running at non-optimal condition, the root cause are traced by variable contributions. Experimental results on a real hot strip mill process (HSMP) illustrate the effectiveness of the proposed method comparing to the traditional distributed SVDD.

Due to the complex static, dynamic, and large-scale characteristics for modern industrial processes, in this article, we propose a double-layer distributed monitoring approach based on multiblock slow feature analysis and multiblock independent component analysis. To this end, the processed dataset is divided into the static and dynamic blocks on the basis of the sequential information of each variable in the first layer. Considering the correlations between the variables in the large-scale processes, the sequential correlation matrices in two blocks are calculated, which serves as the second-layer block division rule. Then, the static and dynamic blocks are further divided into several static and dynamic subblocks in which the variables in each subblock are strongly correlated and in the same state. The slow feature analysis and independent component analysis monitoring models are, respectively, generated for the dynamic and static subblocks. Finally, the monitoring results in each subblock are integrated by Bayesian inference to get the final statistics. The average fault detection rate of the proposed method for the Tennessee Eastman process is 0.842, while those of the other traditional methods are lower than 0.75, which shows the advantages of the proposed method.

In this article, a novel feature-extraction-based process monitoring method is proposed for multimode processes with common features. Different from the traditional feature extraction methods that consider either common scores or common weightings between different modes, a common-subspace-based method that takes both common scores and weightings into account is developed based on tensor decomposition. In addition, specific features for each mode are extracted by the independent component analysis. Moreover, a moving-window Kullback-Leibler-divergence-based detection statistic is developed to monitor the changes in both common and specific features. The newly proposed methods are applied to a real hot rolling mill (HRM) process, where common setting for different steel slabs and specific configurations for each steel product exist. The practical application performance shows that the proposed methods can accurately capture common features and effectively monitor different fault cases in an HRM process.

This article develops a robust fault tolerant (FT) control scheme for an n-link uncertain robotic system with actuator failures. In order to eliminate the influence of both the uncertainties and actuator failures on the system performance, the Gaussian radial basis function neural networks are used to compensate for the actuator failures and uncertain dynamics. An adaptive observer is designed to compensate for external disturbance. In addition, in order to accelerate the recovery of system stability after failure, a nonsingular fast terminal sliding mode is given. Finally, the simulation results on a two-link manipulator confirms the superior performance of the proposed neural networks-based FT controller, and the experiment results on the Baxter robot further verify the effectiveness of the control method.

In this article, two performance-based fault-tolerant control strategies are investigated for multiplicative faults in industrial processes. This is motivated by the fact that the changes in the system parameters caused by malfunctions generally lead to multiplicative faults, which may cause remarkable changes in system dynamics and performance. To be specific, the representation forms of the faulty plants are first given in terms of the so-called stable image and kernel representations, respectively. Then, by measuring the fault-induced system performance degradation, two performance-based fault-tolerant control strategies are formulated. Specifically, a residual-driven dynamic controller, which is also called plug-and-play control, is implemented to achieve control performance recovery in the context of stability margin. Finally, a benchmark study is demonstrated to show the efficiency of the proposed methods.

An imbalanced number of faulty and normal samples make the traditional supervised classification methods difficult to ensure their classification performance. Accordingly, semisupervised learning methods have recently become hotspots both in academic research and practical application domains. Different from previous schemes, this paper dedicates on the correlations, common features, and specific features among quality-related coupling faults in manufacturing industries. The main innovations are as follows: first, it is the first time to develop a robust semisupervised classification framework for quality-related coupling faults, which integrates semisupervised multitask feature selection and manifold learning; second, manifold structures and local discriminant information of unlabeled and limited labeled faulty samples are sufficiently explored to improve the classification performance; and third, correlations among quality-related coupling faults are accurately captured, which are crucial for understanding the uniqueness and relationships of them at the feature level. The proposed method is finally validated in a representative manufacturing industry, i.e., hot strip mill process, where detailed simulation processes are presented and better classification performance is shown compared with the existing approaches.

In actual production processes, the occurrence probability of multiple faults is much higher than that of a single fault, which will affect the process industry operating performance and final products quality. This paper is concerned with industrial practices and theoretical approaches for detection and location of key performance indicator (KPI) related multiple faults in process industries. First, a new KPI-related multiple fault monitoring scheme is addressed from the subprocess level based on the developed correlation-based canonical variable analysis model. Then, Bayesian fusion is implemented to form the final monitoring decisions from the plantwide level. After that, a tensor subspace analysis-based discriminant analysis method is proposed for locating the root causes, which will help field engineers to take correction actions and recover the process operations. Finally, the application to a typical industry process, i.e., hot strip mill process, is given to demonstrate the performance and effectiveness of the proposed methods with real industrial data.

This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi-Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

This paper proposes a common and individual (CnI) feature extraction-based process monitoring (PM) method for tracking the operating performance and product quality of processes with multiple operating modes. Different from traditional methods that separately develop PM models concerning only the individual feature of each mode data, the new method seeks to build the PM model simultaneously from all mode data, including to acquire the common subspace that captures the common feature behind different modes, and the individual subspace that reflects the unique feature of each mode. The newly proposed framework is achieved using the conventional principal component analysis (PCA) and partial least squares (PLS) based methods. The resulting CnI-PCA-based operating performance monitoring method and CnI-PLS-based product quality monitoring method are applied to the typical multimode finishing mill process (FMP) where common configuration for all steel products and individual setting for each steel are existing. Finally, the practical application result shows that the proposed method can be preferable to detect and identify different faults in the multimode FMP.

This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore-Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.

In this paper, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

The quality or key performance indicator related prediction and diagnosis cover a wide range of practical requirements from industrial applications. Although much effort has been devoted to establishing an analytical model between operating conditions and quality variables based on the first principals, it is still a challenge in practice due to the complexity of large-scale industrial process. To solve this problem, the data-driven quality related prediction and monitoring schemes are proposed in this paper. In order to overcome the drawbacks of standard approach, our focus is firstly concentrated on the modifications of standard partial least squares. Moreover, under industrial operating conditions, a subspace aided data-driven approach is further utilized to construct a soft sensor in the framework of diagnostic observer based residual generator. The proposed approaches are finally applied to quality based prediction and diagnosis on an industrial hot strip mill process. Application results indicate the effectiveness of the proposed methods and demonstrate improvement in performance compared to the standard technique.

VFH*could not make optimal choice of direction in the narrow stability region. An improved method called IVFH*enlarges certain value (CV), raise more grids, add to relative threshold and introduce the concept of reactive deformation links to improve the original algorithm. IVFH*use Newtonian Physics and Hooker's Law to update the position of the nodes and deform the links in response to the motion of the obstacles, for avoiding dynamic obstacles in dynamic environment.

Hot strip mill is a typical process with high accuracy and high speed. The effective approach to enhance the products competitiveness is to improve the quality and quantity by means of advanced control strategy. A multivariable system comprised of gauge and mass flow of hot strip mill is analyzed in the article. A state-space model about the gauge and mass flow for one stand and inter-stand in a hot strip mill is set up as well. New steady and coordinated control approach on the basis of LQG and H∞of hot strip mill is proposed and the robust characteristics such as perfect robust stabilization, strong disturbance attenuation and coordination properties are better in the latter. Simulation results show the validity of analysis, design and control.