Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems

In this article, the problem of sampled-data control is considered for fuzzy Markovian jump systems (FMJSs) with actuator saturation. Firstly, the mode-dependent lyapunov functional is constructed, which consists of a two-sided closed-loop function. On the basis of this function, stochastically stable criteria are presented in the form of linear matrix inequalities (LMIs). Then, based on the stochastically stable criteria, mode-dependent sampled-data controllers are designed for FMJSs. As a corollary, stable criteria and controller design are also proposed for the general aperiodic sampled-data linear system with actuator saturation. The improved LMI-based optimization conditions are obtained to estimate the region of attraction. Finally, numerical examples are provided to show the significant improvement of the presented method. INDEX TERMS Fuzzy Markovian jump systems, sampled-data control, mode-dependent Lyapunov function, actuator saturation.

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“…Adding right side of (16) and (17) to LW i (t), and considering (14), (15) meanwhile, we obtain that for t ∈ [t k , t k+1 ),…”

Section: Resultsmentioning

confidence: 99%

Sampled-Data Control for Fuzzy Markovian Jump Systems With Actuator Saturation

et al. 2019

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“…Over the past decades, control problem has attracted respectable attention 1‐16 . The effect of the constraints exists in many practical control systems, such as physical stoppages and chemical reactor temperature.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy finite‐time fault‐tolerant control of nonlinear systems with state constraints and input quantization

Pan

Yang

et al. 2020

Adaptive Control & Signal

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Summary This article concentrates on an adaptive finite‐time fault‐tolerant fuzzy tracking control problem for nonstrict feedback nonlinear systems with input quantization and full‐state constraints. By utilizing the fuzzy logic systems and less adjustable parameters method, the unknown nonlinear functions are addressed in each step process. In addition, a dynamic surface control technique combined with fuzzy control is introduced to tackle the variable separation problem. The problem for the effect of quantization and unlimited number of actuator faults is tackled by a damping term with smooth function in the intermediate control law. Finite‐time stability is achieved by combining barrier Lyapunov functions and backstepping method. The finite‐time controller is designed such that all the responses of the systems are semiglobal practical finite‐time stable and ensured to remain in the predefined compact sets while tracking error converges to a small neighborhood of the origin in finite time. Finally, simulation examples are utilized to testify the validity of the investigated strategy.

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“…In modern industries, numerous plants are nonlinear [1][2][3][4][5] and cannot handle by the existing linear control methods [6,7]. In order to tackle such an intractable issue, abundant intelligent control strategies have been developed such as Takagi-Sugeno (T-S) fuzzy control [8,9], neural network [10,11], sliding mode control [12] and adaptive control [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%