Command-filter-based adaptive neural tracking control for strict-feedback stochastic nonlinear systems with input dead-zone

Xu, Ke; Wang, Huanqing; Zhang, Qiang; Chen, Ming; Qiao, Junfei; Niu, Ben

doi:10.1080/00207721.2021.1882614

Cited by 11 publications

(11 citation statements)

References 58 publications

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“…Assumption (Reference 37) The slopes of the dead‐zone output

u

are the same in negative and positive regions, that is

{m}_y={m}_z=m

. Besides, the dead‐zone output

u

cannot be measured.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

Jiang

Wang

2023

Adaptive Control & Signal

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Summary In this paper, a command‐filter‐based fast finite‐time composite adaptive control scheme is proposed for nonlinear systems with input dead‐zone. The neural networks (NNs) are used to approximate the unknown functions of the controlled systems. The series‐parallel nonsmooth estimation models are used to predict the errors. In addition, the tracking error and prediction errors are anastomosed to update the weights of the NNs. To solve the problem of “explosion of complexity,” the command‐filter technology is adopted. Combining the Lyapunov stability theory and the adaptive backstepping algorithm, a composite adaptive backstepping control scheme is proposed. The proposed scheme dissolves the singularity problem which may emerge in the design process and the neural networks approximation performances can be realized as well. Meanwhile, the proposed scheme guarantees the tracking error converges into a small neighborhood of zero in fast finite‐time and the boundedness of all the closed‐loop signals. Finally, the simulation example is provided to verify the effectiveness of the proposed scheme.

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“…Assumption (Reference 37) The slopes of the dead‐zone output

u

are the same in negative and positive regions, that is

{m}_y={m}_z=m

. Besides, the dead‐zone output

u

cannot be measured.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

Jiang

Wang

2023

Adaptive Control & Signal

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“…To address stability problems of discrete-time systems with external disturbances, a multigradient recursive reinforcement learning approach was proposed in Bai et al (2020), and auxiliary systems were constructed to remove the impacts of input saturation. Decomposed the input dead-zone into a linear term and perturbation-like items, an adaptive neural command-filter was established in Xu et al (2021aXu et al ( , 2021b to handle the issues of strict-feedback stochastic systems. Recently, to decrease the conservatism of the developed control schemes, researchers considered the systems with multiple input nonlinearities and proposed some effective control methods.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

Yue,

Niu,

Wang

et al. 2023

RIA

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Purpose This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances. Design/methodology/approach A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function. Findings Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward. Originality/value The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

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“…Since the command filter backstepping approach has been extensively adopted in stochastic non-linear systems control as in Wang et al (2019c; Xu et al, 2021; Zhao et al, 2018), it must be admitted that these control schemes can only guarantee infinite-time stability, which stands for achieving the desired performance when time tends to infinity. In practical applications, driving the system trajectories to the equilibrium point in a finite-time interval is essential in most cases (Li, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In Wang et al (2019c), an adaptive tracking controller is presented for a class of switched stochastic non-linear systems with an asymmetric output constraint, in which the command filter approach is utilized to handle the ''explosion of complexity'' problem and NNs are used to cope with the unknown nonlinear functions and stochastic disturbances. The issue of command filter adaptive tracking control for a class of strict feedback stochastic non-linear systems with input dead-zone is considered in Xu et al (2021), where radial basis function NNs are employed to approximate the unknown non-linearities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive command-filtered finite-time control of non-strict feedback stochastic non-linear systems

Seifi

Sani

2022

Transactions of the Institute of Measurement and Control

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In this paper, an adaptive neural network (NN) finite-time command filter controller for a class of non-strict feedback stochastic non-linear systems has been investigated. Using error compensation signals and NNs, a command filter controller is presented which guarantees that all the signals of the closed-loop system are practical finite-time stable and the output signal tracks the given reference signal under the bounded error. In the design procedure, NNs are employed to approximate unknown non-linear functions, which contain all the state variables of the whole system, in order to avoid excessive and burdensome computations and ensure that the backstepping method works normally for non-strict feedback systems. Meanwhile, the “explosion of complexity” problem caused by the backstepping method is avoided using the command filter approach. The control scheme not only resolves the “explosion of complexity” problem but also eliminates the filtering error in finite-time. Finally, the simulation results are given to prove the effectiveness of the proposed control method.

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Command-filter-based adaptive neural tracking control for strict-feedback stochastic nonlinear systems with input dead-zone

Cited by 11 publications

References 58 publications

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

Command‐filter‐based fast finite‐time composite adaptive neural control for nonlinear systems with input dead‐zone

Hierarchical sliding mode-based adaptive fuzzy control for uncertain switched under-actuated nonlinear systems with input saturation and dead-zone

Adaptive command-filtered finite-time control of non-strict feedback stochastic non-linear systems

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