Adaptive Neural Quantized Control of MIMO Nonlinear Systems Under Actuation Faults and Time-Varying Output Constraints

Zhao, Kai; Chen, Jiawei

doi:10.1109/tnnls.2019.2944690

Cited by 46 publications

(30 citation statements)

References 38 publications

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“…Remark Different from References 1‐32,33‐35, the system (1) in this article is in nonstrict‐feedback form. If the control schemes in References 1‐32,33‐35 are applied to the system (1), the algebraic loop problem will be inevitable. The algebraic loop problem means that if the control design method in strict feedback systems is adopt, the virtual control signal

α_{i}

in nonstrict‐feedback systems for i th subsystem will be a function contains whole sates x = [ x 1 , x 2 , … , x n ] T , but the states x i + 1 , x i + 2 , … , x n are not available at this time.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 96%

“…According to References 28‐35,44, the quantized input q ( u ) of the controlled system can be expressed as follows

\begin{align} q false(u false) = ({, \begin{matrix} lmatrixleft left \\ array u_{i} sgn (u), & array \frac{u_{i}}{1 + κ} < | u | \leq u_{i}, \dot{u} < 0, o r \\ array u_{i} < | u | \leq \frac{u_{i}}{1 - κ}, \dot{u} > 0 \\ array u_{i} (1 + κ) sgn (u), & array u_{i} < | u | \leq \frac{u_{i}}{1 - κ}, \dot{u} < 0, o r \\ array \frac{u_{i}}{1 - κ} < | u | \leq \frac{u_{i} (1 + κ)}{1 - κ}, \dot{u} > 0 \\ array 0 & array 0 \leq | u | < \frac{u_{\min}}{1 + κ}, \dot{u} < 0, o r \\ array \frac{u_{\min}}{1 + κ} \leq | u | \leq u_{\min}, \dot{u} > 0 \\ array q (u (t^{-})), & array othercase \end{matrix}, <...>) \end{align}

…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

“…In Reference 28, the authors first solved the backstepping control problem of uncertain nonlinear systems with quantized input signal. Then many interesting quantized control methods are developed for uncertain nonlinear systems, such as the robust quantized control, 29‐31 the finite‐time quantized control, 32,33 the event‐trigger quantized control, 34,35 and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, different from the nonlinear strict‐feedback systems, the unknown nonlinear functions in every subsystem of the nonlinear nonstrict‐feedback systems are composed of whole system states. If the traditional control methods in References 1‐32,33‐35 are applied to the nonlinear nonstrict‐feedback systems, the algebraic loop problem will be inevitable in the control process. Thus, it is more difficult and challenging to address the control problem of the nonlinear nonstrict‐feedback systems.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy finite‐time quantized control for stochastic nonlinear systems with full state constraints

Zhang

Tong

Sui

2021

Adaptive Control & Signal

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Summary This article studies the adaptive fuzzy finite‐time quantized control problem of stochastic nonlinear nonstrict‐feedback systems with full state constraints. During the control design process, fuzzy logic systems are used to identify the unknown nonlinear functions, integral barrier Lyapunov functions are employed to solve the state constrained problem. In the frame of backstepping design, an adaptive fuzzy finite‐time quantized control scheme is developed. Based on the stochastic finite‐time Lyapunov stability theory, it can be guaranteed that the closed‐loop system is semiglobal finite‐time stable in probability, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to testify the effectiveness of the developed control scheme.

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α_{i}

Section: Problem Formulation and Preliminariesmentioning

confidence: 96%

“…According to References 28‐35,44, the quantized input q ( u ) of the controlled system can be expressed as follows

\begin{align} q false(u false) = ({, \begin{matrix} lmatrixleft left \\ array u_{i} sgn (u), & array \frac{u_{i}}{1 + κ} < | u | \leq u_{i}, \dot{u} < 0, o r \\ array u_{i} < | u | \leq \frac{u_{i}}{1 - κ}, \dot{u} > 0 \\ array u_{i} (1 + κ) sgn (u), & array u_{i} < | u | \leq \frac{u_{i}}{1 - κ}, \dot{u} < 0, o r \\ array \frac{u_{i}}{1 - κ} < | u | \leq \frac{u_{i} (1 + κ)}{1 - κ}, \dot{u} > 0 \\ array 0 & array 0 \leq | u | < \frac{u_{\min}}{1 + κ}, \dot{u} < 0, o r \\ array \frac{u_{\min}}{1 + κ} \leq | u | \leq u_{\min}, \dot{u} > 0 \\ array q (u (t^{-})), & array othercase \end{matrix}, <...>) \end{align}

…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy finite‐time quantized control for stochastic nonlinear systems with full state constraints

Zhang

Tong

Sui

2021

Adaptive Control & Signal

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“…As actuator faults may cause the control performance deterioration or even catastrophic accidents, it is necessary to design a high reliable system in the presence of possible actuator faults. Various adaptive fault-tolerant control methods are proposed for nonlinear systems in [29][30][31][32][33][34][35]. In [29], an adaptive fuzzy fault-tolerant DSC method is proposed, and observers are designed to detect, isolate, and estimate the fault.…”

Section: Introductionmentioning

confidence: 99%

Resilient Anti-disturbance Dynamic Surface Control of Uncertain Strict-feedback Systems Subject to Partial Loss of Actuator Effectiveness

Liu¹,

Yang²,

Chen³

et al. 2021

Preprint

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This paper is concerned with the tracking control of a class of uncertain strict-feedback systems subject to partial loss of actuator effectiveness, in addition to uncertain model dynamics and unknown disturbances. A resilient anti-disturbance dynamic surface control method is proposed to achieve stable tracking regardless of partial actuator faults. First, data-driven adaptive extended state observers are designed based on memory-based identifiers, such that the uncertain model dynamics, external disturbances, and the unknown input gains due to actuator faults can be estimated. Next, a resilient anti-disturbance dynamic surface controller is developed based on recovered information from the data-driven adaptive extended state observers. After that, it is proven that the cascade system formed by the observer and controller is input-to-state stable. Finally, comparative studies are performed to validate the efficacy of the resilient anti-disturbance dynamic surface control method for nonlinear strict-feedback systems subject to partial loss of actuator effectiveness.

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Command filter‐based adaptive neural event‐triggered control of MIMO pure‐feedback systems with full‐state time‐varying constraints

Zhu

Huang

Ding

et al. 2022

Adaptive Control & Signal

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Summary This article is concerned with the issue of adaptive event‐triggered control for a class of pure‐feedback multi‐input multi‐output (MIMO) nonlinear systems with full‐state time‐varying constraints. By using the hyperbolic tangent as nonlinear mapping technique, the uncertain constrained MIMO non‐affine system is changed into a novel unconstrained MIMO system. Dynamic surface control (DSC) strategy is used to solve the issue of “explosion of complexity.” Command filter is adopted to overcome the insufficient of the DSC method by the error compensation mechanism. Adaptive event‐triggered control scheme is developed for the transformed non‐affine system based on relative threshold mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. All the signals in the controlled system are proved to be semi‐globally uniformly ultimately bounded by adding to compensation signals into the whole Lyapunov function. Two simulation examples demonstrate the capability of the proposed control strategy.

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Adaptive Neural Quantized Control of MIMO Nonlinear Systems Under Actuation Faults and Time-Varying Output Constraints

Cited by 46 publications

References 38 publications

Adaptive fuzzy finite‐time quantized control for stochastic nonlinear systems with full state constraints

Adaptive fuzzy finite‐time quantized control for stochastic nonlinear systems with full state constraints

Resilient Anti-disturbance Dynamic Surface Control of Uncertain Strict-feedback Systems Subject to Partial Loss of Actuator Effectiveness

Command filter‐based adaptive neural event‐triggered control of MIMO pure‐feedback systems with full‐state time‐varying constraints

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