Adaptive decentralized fault‐tolerant tracking control for large‐scale nonlinear systems with input quantization

Wang, Cai-Cheng; Yang, Guang‐Hong

doi:10.1002/rnc.4086

Cited by 18 publications

(18 citation statements)

References 48 publications

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“…Assumption is related to the extraction of the core information from the nonlinearities of the system, which can be readily done for practical systems with only crude model information . For Assumption , we consider the case that the actuator suffers from losing actuation effectiveness in that

0 < \underset{true_}{ρ} \leq ρ (t_{ρ}, t) \leq 1

, as considered in several studies . It is noted that

\underset{true_}{ρ}

is the minimum value of

ρ (t_{ρ}, t)

.…”

Section: Modeling and Problem Formulationmentioning

confidence: 99%

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Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Zhao

Lei

Lin

et al. 2020

Intl J Robust & Nonlinear

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In this work, we develop a robust adaptive fault-tolerant tracking control scheme for a class of input-quantized strict-feedback nonlinear systems in the presence of error/state constraints and actuation faults. The problem is rather complicated yet challenging if nonparametric uncertainties and unknown quantization parameters as well as time-varying yet completely undetectable actuation faults are involved in the considered systems. Compared with the most existing approaches in the literature, the proposed control exhibits several attractive advantages: (1) upon using a nonlinear decomposition for quantized input and employing the robust technique for actuation fault, not only the exact knowledge of quantization parameters are not required, but also the actuation fault can be easily compensated since neither fault detection and diagnosis/fault detection and identification nor controller reconfiguration is needed; (2) based on the error/state-dependent unified nonlinear function, the constraints on tracking error and system states are directly handled and the cases with or without constraints can also be addressed in a unified manner without changing the control structure; and (3) the utilization of unified nonlinear function-based dynamic surface control not only avoids the problem of the explosion of complexity in traditional backstepping design, but also bypasses the demanding feasibility conditions of virtual controllers. Furthermore, by using the Lyapunov analysis, it is ensured that all signals in the closed-loop systems are uniformly ultimately bounded. The effectiveness of the developed control algorithm is confirmed by numerical simulations. K E Y W O R D Serror/state constraints, fault-tolerant control, quantized input, robust adaptive control, strict-feedback nonlinear systems, unified nonlinear function INTRODUCTIONIn the past decade, networked control has drawn a great deal of attention as its advantages in low cost of installation and flexibility in system implementation. There are several communication constraints in the control of networked Int J Robust Nonlinear Control. 2020;30:3215-3233. wileyonlinelibrary.com/journal/rnc

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0 < \underset{true_}{ρ} \leq ρ (t_{ρ}, t) \leq 1

, as considered in several studies . It is noted that

\underset{true_}{ρ}

is the minimum value of

ρ (t_{ρ}, t)

.…”

Section: Modeling and Problem Formulationmentioning

confidence: 99%

“…In addition, the plant to be controlled usually contains uncertainties. Various control schemes have been proposed to handle the quantized systems with uncertainties . It is well‐known that adaptive control is a useful method to handle the uncertainties as its ability of online estimation .…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Zhao

Lei

Lin

et al. 2020

Intl J Robust & Nonlinear

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“…In another aspect, quantization generally exists in lots of controlled nonlinear systems, where the system is interacted with quantized information, a quantized control approach possesses sufficient precision and requires low communication rate. Many excellent results have been published, for example, the investigation of uncertain nonlinear system with input quantization was done in Reference , and an adaptive asymptotic tracking control approach was established. In Reference , the finite‐time output‐feedback controller was constructed for quantized nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Pan

Chadli

et al. 2020

Intl J Robust & Nonlinear

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This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict-feedback form.Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict-feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised.Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme. K E Y W O R D Sadaptive fuzzy control, asymptotic tracking control, nonstrict-feedback system, state constraints, unknown control direction INTRODUCTIONOn account of the extensively practical applications, studies on adaptive trajectory tracking control design for uncertain nonlinear systems have made a major breakthrough, such as adaptive control, 1-3 sliding mode control, 4-6 robust control, 7-9 fault tolerant control, 10,11 and so on. In addition, backstepping-based adaptive neural control 12-18 and fuzzy control [19][20][21][22][23][24][25][26][27][28][29][30] for nonlinear systems have been well studied in the past few years, in which fuzzy logic systems (FLSs) or neural networks (NNs) were viewed as universal approximators to identify the uncertain system nonlinearities, and then, an adaptive controller was designed by combining with the backstepping technique. Cite several examples, based on FLS's online approximation capability, a new adaptive fuzzy control scheme was presented in Reference 20. Tong et al citebib21 established a fuzzy state observer and proposed a decentralized adaptive fuzzy optimal control approach for nonlinear large-scale systems. In Reference 22, the fuzzy adaptive output feedback controller was constructed for strict-feedback nonlinear systems, where the problem of virtual control coefficients was well handled by using a convex combination technique. Int J Robust Nonlinear Control. 2020;30:3365-3381. wileyonlinelibrary.com/journal/rnc

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“…Nonlinearity is an inherent nature of many practical systems that should not be neglected while modeling the control system. In recent decades, the design and analysis of control problem with Lipschitzian nonlinear conditions have received great attention and some interesting results are reported 8‐10 . Indeed, those nonlinearities occurring in the dynamical systems are tackled and computed with the aid of Lipschitz condition either globally or locally.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient nonfragile control protocol for nonlinear large‐scale systems with input quantization

Tharanidharan

Sakthivel

Ren

et al. 2020

Adaptive Control & Signal

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SummaryThe problem of robust stabilization for a class of nonlinear large‐scale systems with the energy constraints and time‐varying actuator faults is addressed in this article. In the considered system, the stabilization criteria is achieved via a sensor‐network‐based distributed fault‐tolerant controller. Moreover to limit the consumption of energy by the sensors, the measurement size reduction technique and communication rate reduction approach are implemented such that the resulting closed‐loop system is stable within a predefined extended passive performance index level. In particular, the sensor signal quantization technique is used to reduce the frequency and size of the transmitted data packet in the communication sequence. Based on the switched system theory and Lyapunov approach, a new set of sufficient conditions is obtained in the form of linear matrix inequality constraints to ensure the exponential stabilization of the considered system. The developed fault‐tolerant resilient control gains are designed by resorting to the cone complementarity linearization algorithm. Moreover, a nonisothermal continuous stirred‐tank reactor with interconnection is considered as an example to demonstrate the effectiveness of the proposed design technique.

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Adaptive decentralized fault‐tolerant tracking control for large‐scale nonlinear systems with input quantization

Cited by 18 publications

References 48 publications

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Robust adaptive fault‐tolerant quantized control of nonlinear systems with constraints on system behaviors and states

Asymptotic tracking control for constrained nonstrict‐feedback MIMO nonlinear systems via parameter compensations

Energy efficient nonfragile control protocol for nonlinear large‐scale systems with input quantization

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