Observer-Based Adaptive Event-Triggered Control for Nonstrict-Feedback Nonlinear Systems With Output Constraint and Actuator Failures

Cao, Liang; Zhou, Qi; Dong, Guowei; Li, Hongyi

doi:10.1109/tsmc.2019.2895858

Cited by 118 publications

(52 citation statements)

References 46 publications

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“…Remark On account of considering full‐state constraints and unknown control direction in this study, the existing ways in References , and that set up the relation between the norm of variable x i , j and the norm of ϱ i , j cannot be directly used in this paper, so a modified partition of variables method is developed in Lemma , which plays a key role in the subsequent backstepping design.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…In another aspect, the adaptive functions in References and that need to be adjusted online are up to n i for each subsystem, but in this paper, the number of online tuning adaptive parameters has been reduced to two, thus the computation burden is significantly reduced and the algorithm is easily realized in practice. In addition, although the controlled nonlinear systems in References , and are in nonstrict‐feedback configuration, the bad impact of unknown control direction, input quantization, and state constraints were neglected. As a matter of fact it is troublesome to simultaneously consider these cases.…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…The primary advantages of this study are listed as follows: A new Nussbaum‐based adaptive fuzzy controller is devised for the nonstrict‐feedback MIMO nonlinear systems, where constrained states, the unknown control direction, and quantized input are synchronously taken into account in the whole time domain, which is more comprehensive than some subsistent results. Combining with the monotonic increasing property of bounded functions, a modified partition of variables method is given to solve the difficulty of nonstrict‐feedback system form. Although the previous works solved the problem arising from nonstrict‐feedback structure, the issues of full state constraints and unknown control direction have not been considered, and their results cannot be directly applied to our study. In some existing results, the issues of overparametrization existing in backstepping control design have been omitted. Unlike them, the issues of overparametrization are offset by constructing two compensation functions, and symmetric barrier Lyapunov functions (SBLFs) are devised to confine all the system states to predefined ranges. …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Controller Design and Stability Analysismentioning

confidence: 99%

Section: Controller Design and Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…As a special class of hybrid systems, Markovian jump systems have been a focus of study during the past few decades, which can model many actual control processes and systems, such as networked control systems, manufacturing systems, fault‐detection systems, and other systems . It is noticeable that most of the results about Markovian jump systems have been obtained under the assumption of completely accessible knowledge of the transition rates in the jumping process.…”

Section: Introductionmentioning

confidence: 99%

“…Due to a variety of complex factors, sometimes, the system states are not accessible. In order to develop less conservative theories and methodologies, the observer design is a necessary and significant method in addressing such issues . In addition, the Lyapunov‐Krasovskii functional in the work of Zhao et al is given as

V false(ξ false(t false), i false) = ξ^{T} false(t false) P_{i} ξ false(t false) + \int_{t - τ false(t false)}^{t} ξ^{T} false(s false) Q_{1} ξ false(s false) d s

, which may give rise to some conservativeness.…”

Section: Introductionmentioning

confidence: 99%

Observer design for stochastic time‐delayed Markovian jump systems with incomplete transition rates and actuator saturation

Gao

Kao

et al. 2019

Optim Control Appl Methods

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Summary This paper is concerned with observer design for stochastic time‐delayed Markovian jump systems with incomplete transition rates and actuator saturation. By employing mode‐dependent Lyapunov‐Krasovskii functional, an observer‐based feedback controller is designed to guarantee stochastic stability of the corresponding closed‐loop saturated system and an estimation of the domain of attraction in the mean square is expanded. The procedure of deriving observer gain matrices is converted into an optimization problem with constraints of a set of linear matrix inequalities. Finally, the vertical take‐off and landing helicopter model is given to demonstrate the validity of the main results.

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Nonlinear filtering‐based output tracking error constraint control for permanent magnet synchronous motors with complete unknown coefficients

Wang

2022

Adaptive Control & Signal

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This article investigates the problem of the output tracking error constraint performance for permanent magnet synchronous motors with complete unknown coefficients. First, to achieve the output tracking error constraint performance, an improved barrier Lyapunov function combined with exponent-type performance is given. Then, to erase the effects of unknown coefficients, adaptive compensation strategy, fuzzy logic systems, and Nussbaum-type function are utilized, which relax the assumption requirement that the partial coefficients must be known in the related results. Further, to simplify the control design, a novel nonlinear filters-based dynamic surface control method is presented. Unlike the previous linear filters-based ones, it can achieve better control performance. It is proved that by utilizing the proposed method, all the signals in the closed-loop systems are bounded, and also the prescribed output tracking error constraint condition does not violate. Final, simulation example verify the effectiveness of our proposed method.

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Observer-Based Adaptive Event-Triggered Control for Nonstrict-Feedback Nonlinear Systems With Output Constraint and Actuator Failures

Cited by 118 publications

References 46 publications

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

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

Observer design for stochastic time‐delayed Markovian jump systems with incomplete transition rates and actuator saturation

Nonlinear filtering‐based output tracking error constraint control for permanent magnet synchronous motors with complete unknown coefficients

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