In practical manufacturing scenarios, re-entrant manufacturing systems (RMSs) are vulnerable to uncertain workstation faults, which may cause sudden changes in the work-in-process (WIP) level and impair the overall production capacity. To ensure agile response to market demands and to maintain stable system behavior under fault scenarios, an active fault-tolerant control scheme via fault diagnosis for RMSs is developed in this paper. Firstly, a hybrid hyperbolic partial differential equation continuum model is developed to describe the evolution of WIP dynamics, where the workstation faults result in the discarding of defective products during processing. Subsequently, a fault diagnosis scheme is presented to detect and estimate the uncertain faults in real time, by integrating an observer-based fault detection method and an adaptive fault estimation algorithm. Utilizing the estimated fault information, a fault-tolerant control strategy is then proposed to compensate for fault-induced state jumps and achieve agile production control. Finally, a numerical simulation is conducted to demonstrate the effectiveness of the proposed fault-tolerant control approach. It is believed the proposed theory enriches the theory, analysis, design of control circuit systems and has potential of practical implementations to industrial manufacturing systems.

This article proposes a data-driven distributed Kalman filter (DKF)-based sensor fault isolation and estimation scheme for large-scale interconnected dynamic systems, composed of heterogeneous subsystems coupled through a directed topological graph. A local diagnosis unit (LDU) is established for each subsystem, where the data-driven DKF-based residual generator is constructed using local and neighboring process data, effectively decoupling the totally unknown interaction component. Subsequently, fully distributed sensor fault isolation is realized at the subsystem and element levels in simultaneous-fault cases. Both local and neighboring sensor fault isolation can be realized in the LDU, allowing the global system sensor fault isolation with only several key LDUs. Then, the data-driven DKF-based estimator is built in each LDU to estimate sensor faults occurring in multiple subsystems. The distributed Kalman gain is computed in a fully distributed manner, with stability analysis performed locally without overall system knowledge. Finally, the effectiveness and performance of the proposed scheme are validated through case studies on the power network system.

In this article, we investigate fault-tolerant and resilient control approaches for cyber-physical systems within a unified control and detection framework. Particularly, a novel strategy is presented to simultaneously detect and accommodate anomalies in cyber-physical systems subject to multiplicative physical faults and additive integrity cyberattacks. An observer-based cyber-secure system configuration is first analyzed by means of the coprime factorization technique, wherein multiplicative faults are characterized by coprime factor uncertainties. It is revealed that fault- and cyberattack-induced variations possess distinct attributes with respect to the closed-loop dynamics. This observation motivates a collaborative detection scheme to distinguish both types of anomalies. Specifically, a performance-based fault detector is implemented on the plant side, delivering fault detection results to the monitoring and control (MC) side, where an observer-based attack detector operates collaboratively. Subsequently, the local and remote controllers are reconfigured to enhance the fault tolerance and attack resilience against faults and cyberattacks. To provide more independent design freedoms, the residual signal derived from the controller dynamics is incorporated into the Youla parameterization-based stabilizing controller. Finally, the proposed scheme is verified on a leader-follower robot system.

This article investigates the fault detection and isolation (FDI) problem for a class of re-entrant manufacturing systems (RMSs) subject to workstation faults, sensor faults, and measurement disturbances. The system dynamics are first characterized by a hybrid hyperbolic partial differential equation (HHPDE) continuum model. A two-stage observer-based FDI framework is then developed to enable timely and reliable fault diagnosis. In the first stage of this framework, a diagnostic observer equipped with residual evaluation logic is employed, which is capable of detecting the occurrence of faults yet incapable of differentiating between the sensor faults and the workstation faults. Once a fault is detected, the isolation procedure starts as the second stage to distinguish the fault types and localize the fault sources. To be specific, anH-/H∞observer-based isolation scheme is proposed to effectively decouple the sensor faults from the sensor disturbances, by exploiting the dual performance of disturbance attenuation and fault sensitivity; an adaptive observer-based isolation strategy is devised to identify the workstation faults by capturing the associated structural changes in the system dynamics. Finally, the effectiveness and robustness of the proposed methods are validated through comprehensive numerical simulations.

This paper proposes a monitoring scheme for nonlinear dynamic systems characterized by uncertainties and partially known models. We first establish a monitoring framework grounded in nonlinear control theory, specifically utilizing the normalized stable kernel representation (SKR). By analyzing the associated Hamiltonian system, we identify the conditions under which the normalized SKR can be constructed, ensuring optimal uncertainty estimation via the solution of a corresponding Hamilton-Jacobi Equation (HJE). These conditions further lead to the formulation of residual-based monitoring indicators. Subsequently, we present a data-driven implementation of this framework that integrates control-theoretic principles with machine learning, termed control theory-informed machine learning (CTIML). In this approach, neural networks are employed to approximate the HJE solution and the corresponding observer gain. These networks are trained using fault-free operational data, incorporating loss terms designed to enforce the satisfaction of the HJE and related theoretical properties, thus preserving crucial system characteristics like the lossless property. The resulting CTIML-based normalized SKR generates residuals and HJE satisfaction metrics used for online monitoring. Finally, the efficacy of the proposed methodology is validated through a case study.

With the increasing complexity of industrial production systems, accurate fault diagnosis is essential to ensure safe and efficient system operation. However, due to changes in production demands, dynamic process adjustments, and complex external environmental disturbances, multiple operating conditions frequently arise during production. The multi-condition characteristics pose significant challenges to traditional fault diagnosis methods. In this context, multi-condition fault diagnosis has gradually become a key area of research, attracting extensive attention from both academia and industry. This paper aims to provide a systematic and comprehensive review of existing research in the field. Firstly, the mathematical definition of the problem is presented, followed by an overview of the current research status. Subsequently, the existing literature is reviewed and categorized from the perspectives of single-model and multi-model approaches. In addition, typical real-world application scenarios are then summarized and analyzed. Finally, the key challenges and prospects in the field are thoroughly discussed.

This paper delves into the distributed consensus control problem of nonlinear multiagent systems under the influence of actuator deception attacks based on a fixed directed topology. Diverging from the existing research, we develop a new actuator deception attack model, where attack signals are generated by an unmodeled system satisfying the input-to-state stable condition, and the unmodeled system utilizes the output consensus error of the agents and its delayed error information as the system input. In this condition, we put forward a novel distributed output feedback consensus control approach. First, we design the distributed controller with a compensator for the follower by the use of the relevant outputs of the agents, which is independent of the time delay and the states of the unmodeled system. Then, by constructing a new Lyapunov function with an adjustable power parameter, we can regulate the range of the functions describing the false data injected into the actuator. Additionally, through the combination of the exchange supply function method, we establish a strict proof that all agents can achieve exponential leader-following full-state consensus driven by the given controller. Finally, a simulation example is presented to demonstrate the effectiveness of the developed approach.

This paper investigates the fault-tolerant observer design problem for a class of re-entrant manufacturing systems (RMSs) in the presence of workstation faults during the production process. A hyperbolic hybrid partial differential equation (HHPDE) continuum model is constructed to describe the dynamics of RMSs suffering from unexpected workstation faults, by considering that machinery failures of workstations lead to discarding of defective products. In the case that the faults are known, a fault-tolerant impulsive observer is designed for state estimation of the RMSs. In the case that the fault information is uncertain, a diagnostic observer based residual evaluation logic is developed for fault detection first. Upon detecting the faults, an adaptive impulsive observer is then proposed to simultaneously estimate both the system states and the faults. In addition, by using a piecewise Lyapunov function candidate, sufficient stability conditions that guarantee the exponential input-to-state stability (EISS) of the estimation error are formulated in terms of linear matrix inequalities (LMIs). Finally, the feasibility and effectiveness of the proposed strategy are validated through numerical simulations.

Modeling, monitoring and control of nanoparticle generation processes are of critical importance in various industrial applications due to their impact on product quality and process efficiency. While traditional first-principles modeling is foundational, it often falls short due to incomplete knowledge and inherent simplifications. This article presents a novel approach using control theory informed machine learning to enhance the accuracy and reliability of these models. This innovative method is particularly useful in environments with uncertainties and noise, making it well-suited for complex processes in nanotechnology. The incorporation of control-theoretic preknowledge and the integration of kernel-based methods with neural networks enables the designed framework to address the challenges described above. The approach demonstrates significant improvements in predicting key performance indicators quantifying the relevant product parameters. The simulation results validate the effectiveness of the control theory informed machine learning framework, indicating its potential to be a robust and efficient solution for real-time process control in nanoparticle generation.

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.

Traditional intelligent fault diagnosis models are usually capable of diagnosing known types of faults. However, in the field of industrial fault diagnosis in open environments, it is almost impossible to collect training samples that cover all fault categories. Therefore, when encountering unknown types of fault, traditional methods tend to misclassify them as known categories. To address this issue, a causal counterfactual faithfulness generation method is proposed for open-set fault diagnosis of complex industrial processes. Initially, the signal data from fault sensors are processed into graph data composed of nodes and edges. Then, the features of nodes and their adjacent nodes are learned and integrated into graph architecture to generate new fault sample attributes. Subsequently, the causal generative model infers the category features and combines known fault categories to generate counterfactual samples. Finally, the sample’s classification as an unknown category is ultimately determined by testing the principle of consistency. The proposed method can significantly improve the accuracy of open-set diagnosis without affecting the accuracy of closed-set classification. Comparison experiments with multiple baseline models in two fault datasets illustrated that the proposed method shows an improvement in almost all indicators, which ultimately verified the effectiveness of the proposed method in the task of fault diagnosis in open environments.

Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias substructures consisting of irrelevant sensor signals and noise data are simpler compared to causal substructures consisting of fault signals, and GNNs tend to utilize the letter to quickly achieve low loss. However, spurious correlations in the bias substructures will mislead predictions. To address this issue, this study takes the disentanglement of causal and bias substructures as the key to improve model stability. A causal disentangled GNN (CDGNN) is proposed. First, sensor signals are transformed into graph data employing an attention mechanism to capture the interactions between them. Then, a causal disentanglement learning module is designed to extract causal subgraphs from input graphs. Finally, causal subgraph features from different source machines are aggregated to form a complete graph representation. Experimental results on two complex industrial datasets indicate that CDGNN is an effective and stable method for fault diagnosis.

The series includes two parts, articulating the two novel avenues of research on intelligent fault diagnosis (FD) for nonlinear feedback control systems. In Part I of the series, we design a novel FD paradigm by elaborating an invertible neural network (INN) for feedback control systems. With the aid of a left manifold, the core idea behind the INN-based FD scheme is as follows: 1) formulation of residual generator used for FD as a projection of system data onto the null space that has the same dimension as system outputs; 2) in a topological space, elaboration of a homeomorphism that delivers an invertible relationship between system outputs and residual signals when the system input is given; and 3) skillful introduction of both the master and slave objective functions to achieve system/parameter identification with information loseless property. Comparing with the existing FD approaches, the three superior strengths of the proposed FD scheme deserving mentation are as follows: 1) it specializes in nonlinear feedback control systems; 2) it can effectively avoid the overfitting problem when approximating or learning nonlinear system dynamics; and 3) control theory guides the whole design, ensuring the interpretability of the learning process. Finally, two studies on nonlinear systems demonstrate the feasibility of the invertible left manifold (ILM)-based FD strategy. Part I would contribute to the future development of machine learning (ML)-based system identification and explainable FD approaches, and also benefits the right manifold-based FD designs in Part II.

In deep-learning-based process monitoring, obtaining an effective feature representation is a critical step in constructing a reliable deep-learning monitoring model. Conventional deep-learning methods like stacked auto-encoders (SAEs) capture feature representation by minimizing the data reconstruction errors, which lack the expression of essential information and ultimately lead to degradation of the monitoring performance. To solve this problem, variational discriminative SAE (VDSAE) is proposed in this article. First, a variational generative discriminative structure is designed to obtain a reliable prelearned discriminator. Based on this new variational discriminator, the authenticity of the reconstructed data is evaluated as an important criterion for feature learning. Then, an SAE incorporating the prelearned discriminator is trained by both minimizing the reconstruction error and maximizing the data authenticity. In this way, the prelearned discriminator makes the network effectively capture the essential expression of the reconstructed data. The proposed approach enables SAE to learn a better feature representation owing to the excellent reconstruction performance. Finally, the feature representation and fault detection performance of VDSAE are verified in two cases. The results show that the average fault detection rates (FDRs) of the multiphase flow facility and the waste-water treatment process (WWTP) can be improved to 72% and 97%, respectively, compared with the other fault detection methods.

This study addresses the problem of guaranteed cost fault-tolerant fuzzy control for multiline re-entrant manufacturing systems (RMSs) against stochastic disturbances and workstation faults. Initially, a nonlinear hyperbolic impulsive partial differential equation model is employed to describe the complex and hybrid dynamics of RMSs suffering from unexpected faults within the working stations, and then the corresponding approximation T-S fuzzy model is constructed. In what follows, with the aid of the parallel distributed compensation fuzzy control scheme, the main results of stability analysis and controller synthesis for the closed-loop re-entrant manufacturing control system are derived using a timer-dependent Lyapunov functional with spatio-temporal auxiliary variables. It is found that by means of the proposed fault-tolerant control approach, the RMS can be effectively and robustly driven to a desired production mode with steady feeding and production rates while the upper bound of a quadratic cost function is minimized. Finally, the effectiveness of the proposed control approach is validated through numerical simulations.

Existing data-driven bearing fault diagnosis studies are based on strong assumptions: complete fault samples are required. The number of fault data can be more or less, but the data of each fault class must be available. However, such a condition is difficult to meet in the industry. Therefore, this paper addresses an open issue: bearing fault diagnosis with incomplete training data. In other words, only partial fault data are available in the training process. This issue is more in line with the industrial situation, and the issue is worthy of in-depth research. In response to this issue, the Cepstrum-Scale-Distance based Framework (CSD-Framework) is proposed, including C-stage, S-stage, and D-stage. The three stages realized vibration signal transformation, multi-scale adaptive adjustment, and multi-metric distance matching, respectively. This is a general framework, suitable for analyzing vibration signals, and is convenient to be combined with advanced AI algorithms. On this basis, the Multi-Metric-Adaptive-Clustering (MMA-Clustering) algorithm and the Multi-Metric-Weight-Classify (MMW-Classify) algorithm are proposed to form the D-stage of CSD. The proposed method has three advantages: 1) generic; 2) scalable; 3) good ability to classify unseen data. Experimental results showed that the performance of CSD was better than a variety of existing AI algorithms, as well as Ceps-AI methods based on cepstrum and AI algorithms.Note to Practitioners—Diagnose bearing faults by using incomplete fault data (only partial fault diameters). Existing approaches require a complete dataset, that is, each fault (diameter) needs to be available, and to achieve accurate classification of high-similar data through strong learning ability, but it is helpless for unseen fault diameters. This paper proposes a fault diagnosis framework based on cepstrum, which focuses on highlighting key fault features with cepstrum analysis technique, and realizes fault diagnosis based on incomplete data by constructing a multi-scale and multi-metric adaptive distance matching method. The framework is validated on a public dataset and a real-world dataset, where only partial fault data (fault diameter) is required for training.

In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.

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.

In modern industrial systems, components have complex interactions with each other, which makes it become a challenging task to identify the operational conditions of industrial systems. Considering that an industrial system, the embedded components and their interactions can be expressed as nodes and edges in a graph, respectively. Therefore, graph representation algorithms are powerful tools for fault diagnosis of industrial systems. As one of the most commonly used graph representation algorithms, graph neural networks (GNN) mainly follow the law of “learning to attend.” GNN extract training data features learn the statistical correlations between features and labels, resulting in the attended graph favoring for accessing noncausal features as a shortcut for prediction. This shortcut feature is unstable and depends on the data distribution characteristics in the training dataset, which reduces the generalization ability of the classifier. By performing the causal analysis of GNN modeling for graph representation, the results show that shortcut features act as confounding factors between causal features and predictions, causing classifiers to learn wrong correlations. Therefore, to discover patterns of causality and weaken the confounding effects of shortcut features, a causal-trivial attention graph neural network strategy is proposed. First, node and edge representations are given by estimating soft masks. Second, through disentanglement, both causal features and shortcut features are obtained from the graph. Third, the backdoor adjustment of the causal theory is parameterized to combine each causal feature with a variety of shortcut features. Finally, comparative experiments on the three-phase flow facility dataset illustrate the effectiveness of the proposed method.

Data-driven methods for predicting quality variables in wastewater treatment processes (WWTPs) have mostly ignored the slow time-varying nature of WWTP, and they are data-consuming that need a large amount of independent and homogeneously distributed data, which makes it difficult to collect. To address this issue with few-shot and inconsistent distribution, a transfer learning method called transferable deep slow feature network (TDSFN) for time-series prediction is proposed by leveraging the knowledge of relevant datasets. TDSFN extracts nonlinear slow features of WWTP with inertia from the time series through a deep slow feature network and constructs the domain invariant features based on them. Target feature attention is designed in TDSFN to enhance the predictor adaptability to the target domain by assigning weights to the source features based on their similarity to target features. Furthermore, a variational Bayesian inference framework is introduced to learn the parameters of TDSFN. The effectiveness of TDSFN is verified through prediction experiments based on WWTP.

Deep learning (DL) methods have been widely applied to intelligent fault diagnosis of industrial processes and achieved state-of-the-art performance. However, fault diagnosis with point estimate may provide untrustworthy decisions. Recently, Bayesian inference shows to be a promising approach to trustworthy fault diagnosis by quantifying the uncertainty of the decisions with a DL model. The uncertainty information is not involved in the training process, which does not help the learning of highly uncertain samples and has little effect on improving the fault diagnosis performance. To address this challenge, we propose a Bayesian hierarchical graph neural network (BHGNN) with an uncertainty feedback mechanism, which formulates a trustworthy fault diagnosis on the Bayesian DL (BDL) framework. Specifically, BHGNN captures the epistemic uncertainty and aleatoric uncertainty via a variational dropout approach and utilizes the uncertainty information of each sample to adjust the strength of the temporal consistency (TC) constraint for robust feature learning. Meanwhile, the BHGNN method models the process data as a hierarchical graph (HG) by leveraging the interaction-aware module and physical topology knowledge of the industrial process, which integrates data with domain knowledge to learn fault representation. Moreover, the experiments on a three-phase flow facility (TFF) and secure water treatment (SWaT) show superior and competitive performance in fault diagnosis and verify the trustworthiness of the proposed method.

This article studies the distributed adaptive failures compensation output-feedback consensus for a class of nonlinear multiagent systems (MASs) with multiactuator failures allowing unmatched redundancy under directed switching graphs. With estimated information of neighbors, a novel distributed reference generator is designed. To compensate the unmeasured state variables of each agent, a reduced-order dynamic gain filter is constructed. Based on the generator and filter, and using the recursive design method, a distributed adaptive protocol is designed, where the adaptive technique is used to compensate the actuator failures. The proposed scheme can significantly relax conditions on the communication graph, which allows the graph to be disconnected at any time instant. The number of introduced variables in the filter and its dimension is greatly reduced and, thus, reduces the numerical challenge. The output-feedback consensus for nonlinear MASs with actuator failures and possible unmatched actuator redundancy is addressed for the first time. The consensus error can converge to an arbitrarily small set not affected by actuator failures, and the resulting closed-loop system is semiglobally stable. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

This article describes a novel concept to optimize manufacturing systems distributively through data-based learning. We propose a game-theoretic (GT) learning set-up that is incorporated with accessible control code of the programmable logic controller (PLC) to accelerate the optimal policies learning procedures, instead of learning everything from scratch. Therefore, we offer to process the accessible and available control code into a GT-based learning framework which is subsequently optimized in a fully distributed manner. To this end, we employ the recently developed framework of state-based potential games (PGs) and prove that under mild conditions PLC-informed (PLCi) learning forms a state-based PG framework. We conduct the experiment on a laboratory scale testbed in numerous production scenarios. The experiment's results highlight the major potential of using the PLCi GT-learning, which is the reduction of energy consumption of the production timescales and improvement of production efficiency while nearly halven the learning times.

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

This article presents a novel neural network training approach for faster convergence and better generalization abilities in deep reinforcement learning (RL). Particularly, we focus on the enhancement of training and evaluation performance in RL algorithms by systematically reducing gradient's variance and, thereby, providing a more targeted learning process. The proposed method, which we term gradient monitoring (GM), is a method to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself. We propose different variants of the GM method that we prove to increase the underlying performance of the model. One of the proposed variants, momentum with GM (M-WGM), allows for a continuous adjustment of the quantum of backpropagated gradients in the network based on certain learning parameters. We further enhance the method with the adaptive M-WGM (AM-WGM) method, which allows for automatic adjustment between focused learning of certain weights versus more dispersed learning depending on the feedback from the rewards collected. As a by-product, it also allows for automatic derivation of the required deep network sizes during training as the method automatically freezes trained weights. The method is applied to two discrete (real-world multirobot coordination problems and Atari games) and one continuous control task (MuJoCo) using advantage actor-critic (A2C) and proximal policy optimization (PPO), respectively. The results obtained particularly underline the applicability and performance improvements of the methods in terms of generalization capability.

Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.

This article addresses the distributed consensus tracking problem for high-order nonlinear multiagent systems (MASs) with nonidentical output constraints under directed switching graphs. Such a practical and important issue has only been taken into limited consideration by the existing literature. In this article, the heterogeneous followers are considered to suffer from nonidentical output constraints, which take precedence over consensus tracking. To this end, a series of intermediate dynamic variables are introduced for each agent to isolate the effect of topologies switching and estimate the output of the leader. Then, distributed adaptive protocols are designed based on a novel constraint transformation. By using an improved mode-dependent average dwell-time lemma, sufficient conditions on switching graphs are given to achieve consensus tracking. Compared with existing results, the proposed scheme can not only guarantee output constraints but also significantly reduce the communication burden and tolerate a certain degree of graph switching. Finally, the proposed method is verified by the inverted pendulum simulation example.

This article investigates the distributed dynamic output-feedback bipartite consensus for a class of stochastic nonlinear multiagent systems (MASs) with time delays and actuators faults under directed switching graphs. First, a distributed extended state compensator is constructed for each agent to compensate for the consensus errors and actuators’ faults only using the output information of neighbors. Then, based on the compensator, a linear memoryless output-feedback controller is designed. Using a technical lemma and the stochastic Lyapunov stability theory, it is proved that combined with the given constraint conditions on graphs switching the 2nd-moment asymptotic bipartite consensus for the MASs can be achieved. Different from existing results, the proposed compensator can not only save the network bandwidth but also compensate for the actuators’ faults. The proposed scheme also significantly relaxes the conditions on the communication network to directed switching graphs and even allows the graph to be disconnected over some time intervals. The common design parameters under any graphs can avoid the detection of graph switching, which will need global topology information. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method.

This article presents a novel approach for distributed optimization of production units based on potential game (PG) theory and machine learning. The core of our approach is split into two parts: the first part concentrates on the conceptual treatment of modular installed production units in terms of a PG scenario. The second part focuses on the development and incorporation of suitable learning algorithms to finally form an intelligent autonomous system. In this context, we model the production environment as a state-based PG where each actuator of each module has the role of an agent in the game aiming to maximize its utility value by learning the optimal process behavior. The benefit of the additional state information is visible in the performance of the algorithm making the environment dynamic and serving as a connector between the players. We propose a novel learning algorithm based on a global interpolation method that is applied to a laboratory scale modular bulk good system. The thorough analysis of the encouraging results yields to highly interesting insights into the learning dynamics and the process itself. The benefits of our distributed optimization approach are the plug-and-play functionality, the online capability, fast adaption to changing production requirements, and the possibility of an IEC 61131 conforming to PLC implementation.

Remaining useful life (RUL) prediction is a reliable tool for the health management of components. The main concern of RUL prediction is how to accurately predict the RUL under uncertainties. In order to enhance the prediction accuracy under uncertain conditions, the relevance vector machine (RVM) is extended into the probability manifold to compensate for the weakness caused by evidence approximation of the RVM. First, tendency features are selected based on the batch samples. Then, a dynamic multistep regression model is built for well describing the influence of uncertainties. Furthermore, the degradation tendency is estimated to monitor degradation status continuously. As poorly estimated hyperparameters of RVM may result in low prediction accuracy, the established RVM model is extended to the probabilistic manifold for estimating the degradation tendency exactly. The RUL is then prognosticated by the first hitting time (FHT) method based on the estimated degradation tendency. The proposed schemes are illustrated by a case study, which investigated the capacitors' performance degradation in traction systems of high-speed trains.

In this article, a robust asymptotic fault estimation (RAFE) design is proposed for discrete-time interconnected systems with sensor faults. By constructing a singular augmented system, an equivalent description of the considered interconnected systems is presented. Then, a novel RAFE observer is proposed for the singular augmented system. Furthermore, gain matrices of the RAFE observer are calculated based on multiconstrained design. Simulation results are illustrated to show the feasibility of the presented approaches.

In this article, optimal observer-based fault detection (FD) and estimation schemes for Takagi–Sugeno fuzzy systems with process faults are investigated. In particular, an optimal FD scheme for fuzzy systems is proposed first aiming at enhancing the sensitivity to the faults and simultaneously increasing robustness against unknown inputs, which gives the extension of the socalled unified solution to fuzzy systems. To further provide the fault information, a least squares fault estimation scheme is developed. It is noteworthy that, the observers for the proposed FD and estimation schemes are updated online recursively. A case study on the laboratory three-tank system is then given to demonstrate the proposed FD and estimation approaches.

In this article, an integrated design diagram for a stable kernel representation (SKR)-based data-driven fault detection (FD) system and performance criteria is proposed for stochastic dynamic systems in the probabilistic sense. A new distributionally robust FD system is developed using input and output data in the absence of a system model and perfect probability distributions for noises and random faults. To be specific, an SKR-based data-driven primary residual generator is first constructed. By introducing the so-called mean-covariance based ambiguity sets, families of probability distributions of the primary residual in fault-free and the concerned multiple faulty cases are characterized. The FD system design is then formulated as a distributionally robust optimization problem in the sense of minimizing the missed detection rate (MDR) with a predefined upper bound of false alarm rate (FAR). With the aid of worst-case conditional value-at-risk, a matrix-valued distribution independent solution to the targeting FD problem is derived without posing specific distribution assumptions. The developed FD system is, thus, robust against the distributional uncertainties of noises and random faults. Simultaneously, a tighter upper bound of MDR for an identical FAR criterion is achieved in comparison with the vector-valued distributionally robust FD method. An experimental study on a laboratory setup of a three-tank system shows the applicability of the proposed method.

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains.

Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and has been greatly studied and applied. For the monitoring of nonlinear dynamic systems, the deep neural network-aided CCA (DNN-CCA) has received much attention recently, but it lacks a general definition and comparative study of different network structures. Therefore, this article first introduces four deep neural network (DNN) models that are suitable to combine with CCA, and the general form of DNN-CCA is given in detail. Then, the experimental comparison of these methods is conducted through three cases, so as to analyze the characteristics and distinctions of CCA aided by each DNN model. Finally, some suggestions on method selection are summarized, and the existed open issues in the current DNN-CCA form and future directions are discussed.

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

This article is concerned with the development of a new event-triggered parity space fault detection (FD) scheme. A linear discrete-time system model with varying sampling periods is presented for handling the problem of event-triggered FD and a new parity relation is established. Based on this, an event-triggered residual generator is constructed and the generated residual is completely decoupled from event-triggered transmission error. The design of the parity matrix is formulated into an optimization problem and an optimal solution of the parity matrix is obtained by using singular value decomposition. The issue of residual evaluation is also considered in the event-triggering implementation. The novelties of this article are twofold. First, a new event-triggered parity relation is obtained and the parity space-based residual signal achieves complete decoupling with the event-triggered transmission error. Second, the calculation of the parity matrix is independent of event parameters. So the design of the parity space-based residual generator and event generator can be carried out independently. Finally, a simulation example is considered to demonstrate the effectiveness of the proposed method.

The distributed monitoring framework is undoubtedly more suitable for large-scale complex industrial systems. However, most existing distributed monitoring methods ignored the information interaction between the local system and its neighbors. In this article, an improved distributed fault detection framework that considering the communication between subsystems is present. The system decomposition is optimized based on the monitoring performance with mechanism knowledge as constraints. The integration of mechanism and data is helpful to find the appropriate common variables between subsystems. The distributed partial least squares (DPLSs) algorithm is proposed to address the local monitoring challenges caused by the propagation of a common variable. The local monitoring model takes full advantage of the information from neighbors to reduce the uncertainty of the local system. Bayesian fusion performance metrics strategy is implemented to detect system status. The simulation results of the Tennessee Eastman process verify the effectiveness of the proposed scheme.Note to Practitioners—This article attempted to tackle an issue derived from distributed process monitoring of industrial processes. Even in an era of big industrial data, the fusion idea of process data and mechanism knowledge also provides a solution to the process decomposition monitoring strategy. It reduces the computational complexity, corrects the misdirection caused by the false information hidden in the measurements, and further increases the monitoring accuracy. Considering the information flowing and spreading along with the process equipment, common variables are used to describe the interaction between different subsystems. Then, the pretrained monitoring model and the online monitoring strategy are given to promote automatic implementation. The operability and monitoring accuracy of the proposed method is verified. It is suitable for process monitoring of large-scale complex industrial systems.

This paper investigates the fixed-time time-varying formation control problems for heterogeneous multi-agent systems (MASs) composed of multiple Unmanned Ground Vehicles (UGVs) and multiple Unmanned Aerial Vehicles (UAVs) in the presence of actuator faults, parameter uncertainties, matched and mismatched disturbances. Besides achieving the desired formation configurations, each follower can also track the position trajectory produced by the virtual leader within fixed time simultaneously. The difference dynamic characteristics between the heterogeneous agents leads to unbalanced interaction of lumped uncertainties in the communication network, which increases the difficulty of collaborative control. To estimate the mismatched disturbances and lumped uncertainties, a fixed-time observer for each follower is designed, which can guarantee the estimation errors converge to the origin in fixed settling time. Subsequently, by utilizing the backstepping technique and the fixed-time stability theory, an observer-based distributed fixed-time formation controller for each follower in the X- Y axes and the observer-based decentralized fixed-time tracking controllers for follower-UAVs in the Z axes are presented, which are shown to be fixed-time stable even under the influence of actuator faults and mismatched disturbances. Moreover, the fixed-time results can ensure the convergence time is independent of initial conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms.

This paper solves the problem of discrete-time fault-tolerant quantum filtering for a class of laser-atom open quantum systems subject to the stochastic faults. We show that by using the discrete-time quantum measurements, optimal estimates of both the atomic observables and the classical fault process can be simultaneously determined in terms of recursive quantum stochastic difference equations. A dispersive interaction quantum system example is used to demonstrate the proposed filtering approach.

This article addresses the performance-based fault detection (FD) and fault-tolerant control (FTC) issues for nonlinear systems. For this purpose, in the first part of this article, the performance-based FD and FTC scheme is investigated with the aid of the nonlinear factorization technique. To be specific, the controller parameterization for nonlinear systems is first discussed. The so-called fault-tolerant margin is introduced as an indicator of the system fault-tolerant ability. Then, the FD scheme aiming at estimating and detecting the stability performance degradation of the closed-loop system caused by the system faults is developed. Furthermore, to recover the system performance, the performance-based FTC strategy is presented. In the second part of this article, the design approach of the performance-based FD and FTC scheme is studied by applying the Takagi-Sugeno fuzzy dynamic modeling technique. The achieved results are demonstrated in the end by a case study on the three-tank system.

Gaussian process regression (GPR) has shown superiority in terms of state estimation for its nonparametric characteristic and uncertainty prediction ability. Due to its heavy computational complexity, GPR is generally used for small datasets. To efficiently deal with the big data, the sparse spectrum approximation method has been successfully applied to GPR to decrease the computational complexity. However, the stationarity of this method is a strict assumption for data and usually mismatches the industrial processes. In this article, we proposed a sparse nonstationary GPR, which can deal with the nonstationary relationship among samples and make the model more flexible, to settle the aforementioned problems. Furthermore, the performance of the proposed method is evaluated using three public datasets and a sampled diesel engine dataset, and the results show the superiority of our proposed method in terms of accuracy.

The fourteen papers in this special section focus on data-drive management of complex systems via plant-wide performance supervision. Currently, massive amounts of data are continuously being produced by social and industrial activities. Consequently, data-driven techniques have received considerable attention both in industry and academia in recent years, aiding scientists to manage and interpret the available data. The reasons behind such popularity of data-driven techniques are twofold. On the one hand, advanced data processing and information acquisition technologies have been developed to the extent that large amounts of data in different forms are available for big data analysis from descriptive to prescriptive. On the other hand, with the help of machine learning methodologies, the supervision and management systems can provide effective decisions for plant-wide optimal performance. Compared to the conventional model-based techniques, the data-driven ones can not only save the costly modeling procedures but also extract valuable information from available process data for real-time analysis and management. However, there are many complex and challenging problems in the data-driven supervision and management techniques, such as data-driven supervision on the safety, security, and robustness, as well as the performance-supervised management and their distributed designs. The papers in this section target recent results, trends, and practical developments in the data-driven methodologies of plant-wide performance supervision and management for complex systems, especially those related to process monitoring and machine learning activities with their industrial applications.

In this paper, a novel robust fault detection (FD) approach called parity space vector machine (PSVM) is proposed for linear discrete-time systems. Aiming to achieve a tradeoff between false alarm rate (FAR) and FD rate (FDR) simultaneously, we focus our study on an integrated design of parity space-based FD in the context of residual generation and residual evaluation. Without a prior knowledge of the distribution of the unknown inputs, we propose to construct a PSVM model and formulate the underlying FD problem as a distribution-free Bayes optimal classifier, where the FAR and FDR indicate the worst-case classification accuracies of future residuals for the fault free case and faulty case. Then a bank of parity space vectors and corresponding thresholds can be designed integratedly by applying the techniques of the minimum error minimax probability machine and, at the same time, an optimal tradeoff between FAR and FDR is achieved. Finally, the effectiveness of the proposed approach is demonstrated on a longitudinal control system of unmanned aerial vehicle and further comparison with a traditional parity space-based FD is also addressed.

This paper presents a novel approach to incorporate detailed Discrete Element Method (DEM) simulation models into a data-based control system applied to the control of a peristaltic singulation and sortation machine (PSM). The bionic principle of peristaltic is often used in nature for locomotion or transporting of goods. For sortation and singulation of parcels, peristaltic movements provide the advantage of faster operation due to the potential parallelization of the singulation and sortation processes and a far more gentle parcel transport. Beside the mechanical design, a major challenge of the PSM design lies in the development of suitable control algorithms. Due to the difficulties to model the complex behaviour of the independent parts physically, the design process appears to be hardly possible and thus a data-based control design based on reinforcement learning (RL) is proposed. Further, a co-simulation which incorporates a detailed DEM simulation is developed. Particularly, by suitably combining the state-of-the-art actor-critic reinforcement learning (ACRL) and a distributed approach using multiple parallel environments, manageable simulation and training times are ensured. The obtained results show the applicability of a DEM model in a co-simulation framework solving the transportation problem of parcels and also the very good performance of the developed RL-based control approach.

This article addresses performance supervised fault detection (PSFD) issues for industrial feedback control systems based on performance degradation prediction. To be specific, three performance indicators are first introduced based on Bellman equation to predict system performance degradations for industrial processes with the aid of machine learning techniques. Based on them, three PSFD schemes are proposed by embedding the performance indicators as supervising information. In this context, the data-driven implementation of PSFD schemes are investigated for linear systems with unmeasurable state variables. A case study on rolling mill process, a typical benchmark in the steel manufacturing processes, is given at the end of this article to illustrate the applications of the proposed fault detection schemes.

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.

In this article, an optimal data-driven approach is proposed to deal with the problem of distribution independent fault detection (FD) for stochastic linear discrete-time systems. For this purpose, an observer-based residual generator is first constructed using process input and output data. Without exact probability distributions for noises and faults, the so-called confidence sets are constituted in terms of mean and covariance matrix to characterize residual in fault-free and faulty cases. On this basis, a stochastic optimization FD problem is formulated, which allows an integrated design of residual evaluation function and threshold toward maximizing fault detection rate (FDR) for an acceptable false alarm rate (FAR) in the worst-case setting. Furthermore, a data-driven formulation of the underlying FD problem is studied, wherein the estimation uncertainties caused by the deviation of empirical mean and covariance matrix from their real values are concerned. The robustness of the FD system is investigated in the probabilistic context. Confidence levels of the obtained FAR and FDR are achieved quantitatively. The main advantages of the proposed FD approach lie in its independence of probability distributions for noises and faults, the robustness to the estimation uncertainties and the quantitative probabilistic evaluation to the confidence levels of FAR and FDR. A case study on a three-tank system illustrates the effectiveness of the demonstrated approach.

This paper is concerned with a control performance-based fault-tolerant control strategy for singular systems in presence of multiplicative faults. To be specific, the observer-based realization of Youla parameterization is first addressed for singular systems. Then the fault-tolerant margin for closed-loop systems is studied and further modified to bring forth the stability margin degradation, which serves as an indicator for the performance change caused by the faults in the closed-loop. Based on it, a control performance-based fault detection scheme is realized, aiming at detecting those faults that may affect the system stability. Controller reconstruction rule is given afterwards to ensure the system performance when fault occurs. A circuit system model is finally provided to illustrate the validness of the derived control scheme.

This paper addresses fault-tolerant control (FTC) issues for linear systems with model uncertainty and multiplicative faults. The left and right coprime factorization techniques are first adopted for system modeling. Then, the fault detection (FD) approaches are investigated in the coprime factorization context. Based on the information provided by the FD systems, the corresponding FTC architectures and design schemes are presented. Moreover, the gap metric techniques are applied to fault detectability analysis, including the fault detectability indicators to quantify the detection performance in the presence of model uncertainty. The effectiveness of the developed methods for industrial application is illustrated by a case study on a dc motor.

We present a novel approach for distributed optimization of highly flexible, modular production units enabling plug-and-play production with online optimization capabilities to adapt fast to changing production requirements. The approach is fully distributed in the sense that each production module to be optimized is equipped with its own optimization agent which local objective is the optimization of its own production objectives. To assure the necessary coordination between the agents, the resulting distributed optimization problem is designed using concepts of game theory. To this end, we model the production environment in terms of a potential game where each module is modeled as a player of the game. By assigning suitable utility functions to the players coordination of the agents behavior is achieved to find an optimal collective behavior. We apply the approach to a laboratory scale distributed bulk good production testbed with very encouraging results. In addition, due to the computational simplicity of the approach, an implementation in IEC61131 compatible code is possible allowing a direct implementation of the approach in existing production units.

In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. This paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of the data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible.

This paper deals with a real-time-weighted observer-based fault-detection (FD) scheme for Takagi-Sugeno (T-S) fuzzy systems. The essential idea is to develop a weighted diagnostic observer-based FD system to optimize the worst case robustness and fault sensitivity simultaneously by using the information provided by each local system. To achieve an early detection of potential fault, the robustness issue is investigated in the L∞/L2observer-based FD context. Meanwhile, the L-fault sensitivity condition is addressed to optimize the fault detectability. Using fuzzy Lyapunov functions, sufficient conditions on the FD system design are studied. Two examples are given in the end to show the efficiency of the proposed results.

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 addresses the finite horizon H∞fault estimation problem for 2-D linear discrete time-varying systems with bounded unknown input and measurement noise. The main contribution of this paper is the H∞fault estimator for 2-D systems with a necessary and sufficient existence condition. By introducing a partially equivalent stochastic dynamic system in Krein space, the necessary and sufficient condition for the existence of the H∞fault estimator is derived based on innovation analysis and projection formula in Krein space. Then, the solution of the estimator is achieved by means of a Riccati-like difference equation for 2-D systems. Finally, a thermal process example is given to demonstrate the effectiveness of the proposed method.

In this paper, after a brief introduction on the proposed plug-and-play (PnP) process monitoring and control system, two online configuration approaches are proposed for the observer-based residual generator. Firstly, an adaptive residual generator is developed based on the adaptive observer scheme. Aiming at higher feasibility and reliability of industrial application, an iterative learning approach is later developed. The well established three-tank benchmark system is utilized for the demonstration of the effectiveness of the proposed approaches.

This paper presents a novel approach to operate industrial robots as used for manufacturing lines within a cooperative robot station. The proposed framework consists of the application of especially to the cooperative robot handling problem adjusted Reinforcement Learning (RL) algorithms. Such RL-algorithms deal with sequential decision making processes in a trial-and-error learning interaction with the environment, to finally gain an optimal team-working behavior among the robots. In particular application results to a real team-working robot station underline the effectiveness of the novel RL approach.

This paper presents a novel approach to the optimization of energy consumption in large scale industrial bulk good processes. The approach is based on a model-free self-learning algorithm solely based on available process data using ideas from the well known reinforcement learning framework. To this end energy consumers of the plant are integrated in the optimization framework such that each consumer learns its own optimal energy profile for a given production task. The approach is implemented on a laboratory size testbed where the task is the supply of bulk good to a subsequent dosing section. The capability of the approach is underlined by the results obtained at the testbed.

This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an £∞/£2type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the £∞/£2observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

This paper is concerned with the analysis and integrated design of a type of observer-based fault detection (FD) system for general nonlinear processes. To this end, the existence and design condition for this type of nonlinear observer-based FD systems is first introduced. In this context, the integrated design scheme is investigated by dealing with the design condition with the aid of Takagi-Sugeno (T-S) fuzzy dynamic modeling technique. To be specific, the universal T-S fuzzy observer-based residual generator is developed via fuzzy Lyapunov functions. Subsequently, an integrated observer-based FD scheme is proposed with an embedded dynamic threshold, which is generated to meet the real-time FD requirements from industrial processes. In the end, a numerical example and case simulation study on a continuous stirred tank heater process are performed to show the application of the proposed method.

The main focus of this paper is on the analysis and integrated design of $\mathcal {L}_2$ observer-based fault detection (FD) systems for discrete-time nonlinear industrial processes. To gain a deeper insight into this FD framework, the existence condition is introduced first. Then, an integrated design of $\mathcal {L}_2$ observer-based FD approach is realized by solving the proposed existence condition with the aid of Takagi-Sugeno fuzzy dynamic modeling technique and piecewise-fuzzy Lyapunov functions. Most importantly, a weighted piecewise-fuzzy observer-based residual generator is proposed, aiming at achieving an optimal integration of residual evaluation and threshold computation into FD systems. The core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors. In comparison with the standard norm-based fuzzy observer-based FD methods, the proposed scheme may lead to a significant improvement of the FD performance. In the end, the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant.

This paper investigates the problem of output feedback robust ℋ∞control for a class of nonlinear spatially distributed systems described by first-order hyperbolic partial differential equations (PDEs) with Markovian jumping actuator faults. The nonlinear hyperbolic PDE systems are first expressed by Takagi-Sugeno fuzzy models with parameter uncertainties, and then, the objective is to design a reliable distributed fuzzy static output feedback controller guaranteeing the stochastic exponential stability of the resulting closed-loop system with certain ℋ∞disturbance attenuation performance. Based on a Markovian Lyapunov functional combined with some matrix inequality convexification techniques, two approaches are developed for reliable fuzzy static output feedback controller design of the underlying fuzzy PDE systems. It is shown that the controller gains can be obtained by solving a set of finite linear matrix inequalities based on the finite-difference method in space. Finally, two examples are presented to demonstrate the effectiveness of the proposed methods.

A stabilizing regulator designed by any technique whatsoever can be viewed as the combination of a state estimate feedback controller and additional feedback from a dynamic compensator driven by the residual signal (difference between actual and estimated system outputs). Motivated by such an observation, this paper presents the controller design from the premise that system stability is first guaranteed. Control performance can then be enhanced by the optimal design of a residual-driven dynamic compensator subject to some quadratic performance index. The resulting compensator design methods are carried out using both offline and online iterative algorithms with guaranteed convergence. Moreover, the final iterative realization strategy can be implemented online with observed state variables and input updates in case of unknown system dynamics or parameter changes. Simulation results are presented to illustrate design procedures as well as the feasibility of our proposed scheme.

In this paper, a design framework of fault-tolerant wireless networked control systems (NCSs) is developed for industrial automation applications. The main objective is to achieve an integrated parameterization and design of the communication protocols, the control and fault diagnosis algorithms aiming at meeting high real-time requirements in industrial applications. To illustrate the design framework, a laboratory wireless fault-tolerant NCS platform is presented.

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 robust control problem is studied for a class of large-scale networked control systems. The subsystems are in the nonlinear form, and they exchange information through the communication networks. The interconnections considered are nonlinear, and not the traditional linear form, which brings a challenging issue for the decentralized control design. We develop a new memoryless control scheme with the use of the decomposition for each subsystem that is based on the input matrix. By Takagi–Sugeno (T–S) fuzzyfication for each subsystem, the interconnected T–S fuzzy subsystems are obtained. When the upper bound functions of uncertain interconnections are known, we design a decentralized memoryless state feedback controller. When the parameters of bound functions are not available, the adaptive method is used, and the decentralized memoryless adaptive controller is developed. By the construction of a new Lyapunov–Krasovskii functional, we prove the stability of the resultant closed-loop system for the both cases. Finally, we apply the theoretic results to the decentralized controller design of networked interconnected chemical reactor systems. The simulations are performed, and the effectiveness of the proposed method is demonstrated.

A novel fuzzy-observer-design approach is presented for Takagi-Sugeno fuzzy models with unknown output disturbances. In order to decouple the unknown output disturbance, an augmented fuzzy descriptor model is constructed by supposing the disturbance to be an auxiliary state vector. A fuzzy state-space observer is next designed for the augmented fuzzy descriptor system, and the simultaneous estimates of the original state and disturbance are thus obtained. The proposed observer technique is further applied to estimate sensor faults. Finally, a numerical example is given to illustrate the design procedure, and the simulation results show the desired tracking performance. The preknowledge of the disturbance and fault is not necessary for our design. Moreover, the considered disturbance and sensor fault can be in any form.

This paper studies the problem of designing a robust fault-detection system for uncertain Takagi-Sugeno fuzzy models. The worst case fault sensitivity measure is formulated in terms of linear matrix inequalities. The existence of a robust fault detection system that guarantees i) the L2-gain from a fault signal to a residual signal greater than a prescribed value and ii) the L2-gain from an exogenous input to a residual signal less than a prescribed value is given in terms of the solvability of linear matrix inequalities. Numerical examples are used to illustrate the effectiveness of the proposed design techniques.

Problems related to the fault detection of networked control systems are studied. The influence of network-induced delay on conventional observer based fault detection systems designed without considering it is first evaluated, then a parity relation based fault detection system robust to that kind of delay is proposed and studied.