Adaptive neural dynamic surface control of MIMO uncertain nonlinear systems with time-varying full state constraints and disturbances

Yan, Wei; Zhou, Pingfang; Wang, Yueying; Duan, Dengping; Zhou, Weixiang

doi:10.1016/j.neucom.2019.07.033

Cited by 39 publications

(27 citation statements)

References 47 publications

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“…In practice, the output/state of nonlinear systems are often subject to various constraints, such as aerial vehicles (Wei et al, 2019; Zheng and Xie, 2019), robot manipulation systems (He et al, 2018; Tang et al, 2016), and marine vehicles (Zhang and Wu, 2020). The transgression of constraints may reduce system performance or even lead to dangerous.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Yan

Zhou

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper investigates the issue of finite-time tracking control for multiple-input–multiple-output nonlinear systems subject to uncertainties and full state constraints. To deal with full state constraints directly, integral barrier Lyapunov functionals (iBLF) are introduced. By using finite-time stability theory, an iBLF-based adaptive finite-time neural control scheme is presented. To solve the problem of “explosion of complexity” in the design of traditional backstepping control, a new finite-time convergent differentiator is presented. Through stability analysis, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded, the finite time convergence can be guaranteed, and the state constraints are never violated. Finally, the attitude tracking simulations for an autonomous airship are conducted to verify the effectiveness of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Yan

Zhou

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

Self Cite

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“…It introduces a first-order lowpass filter to calculate the derivative of the virtual control and eliminates the "differential explosion" phenomenon which can easily be generated in the backstepping control, simplifying the controller and calculation amount, and achieving great results in many practical engineering problems. Reference [6] researched an adaptive neural network dynamic surface control method to achieve tracking control of a 6-DOF unmanned airship. Reference [7] proposed a neural networks-based finite-time dynamic surface position tracking control method for induction motors with input saturation in electric vehicle drive systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Dynamic Surface Output Feedback Control for Nonlinear Full States Constrained Systems

Wan

2020

IEEE Access

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In order to solve the control problem of uncertain nonlinear systems with state constraints, a dynamic surface output feedback control technology based on Radial Basis Function (RBF) neural networks state observer is proposed. The state observer is designed to estimate the unknown state of the systems by using the approximation characteristics of RBF neural networks, and to constrain the system state by using the Barrier Lyapunov Function (BLF). Based on the backstepping control, a first-order low-pass filter is introduced to design a dynamic surface control (DCS), which solves the "differential explosion" phenomenon that can easily occur in backstepping control. Finally, the stability of the closed-loop system which is confirmed by the Lyapunov method guarantees the semi-globally uniformly ultimately boundedness (SGUUB) of all the signals. The effectiveness of the methods that the boundedness of the tracking errors, the observer states and the controllers can be guaranteed, and good control performance could be achieved is shown by simulation results. INDEX TERMSDynamic surface control, full state constraints, nonlinear constrained systems, RBF neural networks, state observer.

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“…For each subsystem, FDO is used to compensate disturbances, so that the disturbance estimation error converges. In order to further improve adaptability of controller, intelligent neural networks have also been widely employed [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Surface Control for Aircraft With Multiple Disturbances Based on Radial Basis Network

Liu

et al. 2020

IEEE Access

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In this paper, an adaptive dynamic surface control (DSC) method based on neural network for the flight path angle of an aircraft is investigated in view of the parameters uncertainty, multi-disturbance and nonlinearity of the aircraft. First, a traditional backstepping controller is derived as a base. To enhance the adaptability and robustness, radial basis function (RBF) neural networks are introduced to estimate the unknown parameters of the model online and overcome the external disturbance. In addition, two first-order low-pass filters in the dynamic surface control, which can eliminate the expansion of the differential terms and simplify the design of controller's parameters, are devised to compute the derivative of the virtual controller. Then the parameters range of the dynamic surface controller is obtained by stability analysis, which is convenient for us to opt for and regulate these parameters independently. Eventually, the semiglobal stability of closed-loop system is rigorously proved by Lyapunov method. And the simulation results of dynamic surface control and backstepping control under multiple groups of different disturbances also manifest that the derived dynamic surface controller possesses faster and more precise tracking performance, stronger adaptive ability and robustness to external disturbances than backstepping controller. Generally speaking, the adaptive dynamic surface controller engineered in this paper has considerable reference significance for the control of practical aircraft. INDEX TERMS Adaptive neural network, dynamic surface control, disturbances, backstepping, aircraft.

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Adaptive neural dynamic surface control of MIMO uncertain nonlinear systems with time-varying full state constraints and disturbances

Cited by 39 publications

References 47 publications

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Adaptive finite-time neural backstepping control for multiple-input–multiple-output uncertain nonlinear systems with full state constraints

Adaptive Neural Dynamic Surface Output Feedback Control for Nonlinear Full States Constrained Systems

Adaptive Dynamic Surface Control for Aircraft With Multiple Disturbances Based on Radial Basis Network

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