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In this article, a novel active fault‐tolerant control (AFTC) design method is developed by using the switching linear parameter varying (LPV) controller with inexact fault‐effect parameters. The LPV controller is not only considered as a gain‐scheduled controller varying with the operating points, but also an AFTC controller varying with the fault‐effect parameters, which can be obtained via the fault estimator in real‐time. The inexact measurement of fault‐effect parameters is considered for the first time in this article. To cover large parameters variation, a class of switched LPV fault‐tolerant controller is designed to work in the multiple partitioned parameters subregions. The dynamic output feedback controller model is constructed to perform the switched LPV fault‐tolerant control task under the mode‐dependent average dwell time constraint. By using the multiple parameter‐dependent Lyapunov function approach, sufficient conditions with less conservatism are obtained, such that the corresponding closed‐loop systems are globally uniformly exponentially stable and satisfy an upper bound of weighted l2‐gain performance indexes. Finally, effectiveness and applicability of the developed approaches are validated via an active magnetic bearing system. [ABSTRACT FROM AUTHOR]

This paper studies the stabilizability and instabilizability problems for delayed complex-valued memristive neural networks within finite-time intervals. First, more general assumptions for complex-valued activation functions are given. To check that whether the closed-loop system is stable within a finite-time interval, a novel nonlinear delayed controller with separable real-imaginary parts is designed. It includes two independent parameters different from the existing ones, which makes the controller more general but also leads to great difficulties. To overcome these difficulties, two new inequalities are proposed and proved. Then, through Lyapunov function approach, sufficient conditions are derived for the finite-time stabilizability of the closed-loop system and the settling time is estimated. Accordingly, some criteria for the finite-time instabilizability are also established by adjusting different parameters in the designed controller. Finally, several numerical simulations are given to show the effectiveness and advantages of the proposed results. [ABSTRACT FROM AUTHOR]

This study is concerned with the data‐based control of networked non‐linear control systems with random packet dropouts in both the sensor‐to‐controller and controller‐to‐actuator channels. By taking advantage of the characteristics of networked control systems such as the packet‐based transmission, timestamp technique, as well as smart sensor and actuator, a data‐based networked predictive control (DBNPC) method is proposed to actively compensate for the two‐channel packet dropouts, where only the input and output data of the non‐linear plant are required. A sufficient condition for the stability of the closed‐loop system is developed. Furthermore, the resulting DBNPC system can achieve a zero steady‐state output tracking error for step commands. Finally, extensive simulation results on a networked non‐linear system demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study studies the problem of setpoint output tracking for networked control systems with network‐induced delay, packet disorder and packet dropout. Based on the input–output difference equation model, a novel networked predictive control (NPC) method via static output feedback integral controller (SOFIC) is proposed to actively compensate for the round‐trip time (RTT) delay resulting from the aforementioned communication constraints. It is analysed that the resulting NPC system can achieve a similar tracking performance as that of the corresponding local control system. Based on the performance analysis, a necessary and sufficient condition for the stability of the closed‐loop NPC system is obtained, which is independent of the RTT delay. Both numerical simulations and practical experiments on an internet‐based servo motor system illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies the problem of output tracking for networked control systems with network-induced delay, packet disorder, and packet dropout. The round-trip time (RTT) delay is redefined to describe these communication constraints in a unified way. By including the output tracking error as an additional state, the output tracking problem is converted into the stabilization problem of an augmented system. Based on the observer of the original state increment and the feedback of the output tracking error, a model-based networked predictive output tracking control (NPOTC) scheme is proposed to actively compensate for the random RTT delay. The closed-loop stability is proved to be independent of the RTT delay, and the separation principle for the design of the observer-based state feedback controller is still held in the NPOTC system. A two-stage controller design procedure is presented, which not only guarantees the stability of the closed-loop NPOTC system but also achieves the same output tracking performance as that of the local control system for time-varying reference signals. Both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of delay-range-dependentiterativelearning reliable guaranteed cost control of a class of batch processeswith uncertainties, state delay and actuator failures, where the maincontribution is in the relevant concept of H∞iterative learning fault-tolerant guaranteed costcontrol based on an equivalent two-dimensional system descriptionof these processes. The H∞iterativelearning reliable guaranteed cost controller (ILRGCC) is formulatedto include a robust extended feedback control for ensuring the performancesover time and an iterative learning control (ILC) for improving thetracking performance from cycle to cycle, such that it can not onlyguarantee the closed-loop convergence along both the time and thecycle directions but also satisfy both the H∞performance index and a cost function preserving upperbounds for all admissible uncertainties and any actuator failures.To achieve the least guaranteed cost for the closed-loop system, aconvex optimization problem with linear matrix inequality (LMI) constraintsis formulated for the design of the optimal guaranteed cost controllersuch that the delay-range-dependent existing condition of the proposedILRGCC design can also be given by LMIs, where the lower and upperdelay bounds only change along time tdirection.An illustrative example of applications to the nozzle packing pressurecontrol in injection molding has been developed to demonstrate theeffectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Based on a two-dimensional (2D) Fornasini–Marchsini system description of a batch process in industry, a robust state feedback integrated with an iterative learning reliable control (FILRC) scheme is proposed. The scheme is intended for batch processes with uncertain perturbations and state delay subject to actuator failures, which is dependent on the upper and lower delay bounds of the interval time-varying delay. The relevant concepts of fault-tolerance are introduced. The proposed control law can guarantee closed-loop convergence along both time and cycle directions to satisfy H ∞ performance even with unknown disturbances and actuator failures. By introducing a new 2D Lyapunov–Krasovskii functional candidate and adding a differential inequality without introducing redundant free-weighting matrices to the difference Lyapunov functional for 2D systems possessing two directions, conditions for the existence of the proposed FILRC scheme are established in terms of linear matrix inequalities (LMIs). By solving these LMIs, the FILRC law is explicitly formulated together with an adjustable robust H ∞ performance level. Applications to injection velocity control show that the proposed FILRC achieves the design objectives well. [Copyright &y& Elsevier]

This study deals with a fundamental issue of evaluating the performance of widely used fault detection and diagnosis (FDD) schemes within a closed‐loop framework. The focus is to examine how certain implemented controller would impact on the FDD performance and how such performance can be further improved. For this purpose, the authors consider the FDD problem for a class of linear discrete‐time systems (with and without unknown disturbances) under typical proportional–integral control using observer‐based methods. It is revealed that some existing observer‐based FDD approaches are no longer applicable in the closed‐loop situation due to the feedback control. To solve the problem, by appropriately modifying the structure and re‐designing the parameters of the observers, it is proven that the dynamics of closed‐loop residuals can be made identical with those of the residuals obtained with known control inputs at each time step. A numerical example is provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]