Adaptive Backstepping Control of Uncertain Nonlinear Systems With Input and State Quantization

Wang, Wei; Zhou, Jing; Chen, Wen; Long, Jiang

doi:10.1109/tac.2021.3131958

Cited by 54 publications

(13 citation statements)

References 32 publications

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“…Remark 1: Actually, the research for the stabilization of strict-feedback systems with states and input quantization has been lately raised in [23]. The distinctions between this paper and [23] are exhibited below.…”

Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 96%

“…(i) The system studied in [23] only considers partially unknown parameters, while this work studies the uncertain systems with the entirely unknown function and utilize the RBFNN to tackle them.…”

Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 99%

“…(ii) In comparison with [23], this paper additionally considers the issues of random perturbations and input delay. The Pade approximation transformation technique is introduced to tackle the input delay.…”

Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 99%

“…where ē = [e 1 , • • • , e n ] T , ℏ x and ℏ ν denote positive constants, σ and ∆ are positive constants given later. Lemma 3: [23] The influence of system states quantization meets properties as below…”

Section: B Mathematical Lemmasmentioning

confidence: 99%

“…However, the current research on nonlinear systems with both input quantization and quantized states is scarce. Wang et al [23] proposed a novel adaptive backstepping control strategy for the uncertain system with both states and input quantization. But the system function in [23] must be known, the random noise and time delay have not been considered.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive NN Control of Input-Delayed Stochastic Nonlinear Systems With States and Input Quantization

Yang

et al. 2022

IEEE Access

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In this article, the adaptive neural control problem is studied for the input-delayed stochastic nonlinear system that has both states and input quantization. The main difficulty is to deal with the discontinuity of the virtual control laws caused by states quantization, and to compensate the influence of the input quantization by building the relation between the control input and quantized input. Moreover, this paper considers a sector bounded quantizer and proposes a new adaptive neural network (NN) control algorithm to handle the problem of quantization errors cannot be automatically bounded. For the unknown function in stochastic strict-feedback systems, the radial basis function neural network (RBFNN) is utilized to approximate it. The Pade approximation technique is employed to tackle the issue of input delay. All the closed-loop signals can maintain semi-globally uniformly ultimately bounded (SGUUB) in probability under the constructed controller and quantizer. Lastly, the efficiency of the developed control method is evaluated by a simulation experiment.INDEX TERMS Stochastic nonlinear system, states and input quantization, input delay, adaptive neural control.

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Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 96%

Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 99%

Section: Problem Formulation and Some Preliminaries A Problem Formula...mentioning

confidence: 99%

Section: B Mathematical Lemmasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive NN Control of Input-Delayed Stochastic Nonlinear Systems With States and Input Quantization

Yang

et al. 2022

IEEE Access

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Fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems with full state constraints

Zhao,

Sun

et al. 2023

Adaptive Control & Signal

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SummaryThis article focuses on the fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems. Random disturbances with constraints have rarely been considered in previous studies of fast finite‐time stabilization problems. The corresponding extension of the adaptive fast finite‐time stabilization mechanism to stochastic nonlinear systems with full‐state constraints is one of the innovations of this article. First, neural networks and barrier Lyapunov functions are utilized to handle the problems arising from the unknown nonlinear terms and the state constraints, respectively. Second, an adaptive fast finite‐time tracking control scheme for stochastic systems is designed based on the backstepping technique and a fast finite‐time control strategy, which allows all signals of the closed‐loop system to be bounded. Meanwhile, the designed controller guarantees a fast convergence of the tracking error to a small neighborhood of the origin under full‐state constraints. Finally, two examples are given to illustrate the feasibility of the proposed method.

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Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

Ma,

Li,

Ning

et al. 2024

Adaptive Control & Signal

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SummaryThis article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

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Adaptive Backstepping Control of Uncertain Nonlinear Systems With Input and State Quantization

Cited by 54 publications

References 32 publications

Adaptive NN Control of Input-Delayed Stochastic Nonlinear Systems With States and Input Quantization

Adaptive NN Control of Input-Delayed Stochastic Nonlinear Systems With States and Input Quantization

Fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems with full state constraints

Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

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