Adaptive control of pure‐feedback nonlinear systems with full‐state time‐varying constraints and unmodeled dynamics

Huang

Asian Journal of Control

et al. 2023

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The finite-time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full-state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full-state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are semi-globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.

Section: Introductionmentioning

confidence: 99%

Finite‐time command filter‐based adaptive tracking control for nonstrict feedback nonlinear systems with full‐state restrictions and unmodeled dynamics

Huang

Asian Journal of Control

et al. 2023

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“…Normalized signal was designed to restrict the input unmodeled dynamics, and the disturbance produced by it is effectively suppressed. 7,8 A backstepping based DSC design for a class of nonlinear systems in pure-feedback form with arbitrary uncertainty was introduced in the work of Wang et al 9 Adaptive neural DSC scheme was investigated for a class of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics. 10 There usually exist several constraints in practical systems.…”

Section: Introductionmentioning

confidence: 99%

Command filter‐based adaptive neural event‐triggered control of MIMO pure‐feedback systems with full‐state time‐varying constraints

Huang

Adaptive Control & Signal

et al. 2022

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Summary This article is concerned with the issue of adaptive event‐triggered control for a class of pure‐feedback multi‐input multi‐output (MIMO) nonlinear systems with full‐state time‐varying constraints. By using the hyperbolic tangent as nonlinear mapping technique, the uncertain constrained MIMO non‐affine system is changed into a novel unconstrained MIMO system. Dynamic surface control (DSC) strategy is used to solve the issue of “explosion of complexity.” Command filter is adopted to overcome the insufficient of the DSC method by the error compensation mechanism. Adaptive event‐triggered control scheme is developed for the transformed non‐affine system based on relative threshold mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. All the signals in the controlled system are proved to be semi‐globally uniformly ultimately bounded by adding to compensation signals into the whole Lyapunov function. Two simulation examples demonstrate the capability of the proposed control strategy.

“…13 Different from controllers based on integral BLF or logarithmic BLF, NM was more convenient by using hyperbolic tangent function to deal with the constrained problem. 14 By introducing one-one NM to handle system with asymmetric and time-varying output constraints, 15 adaptive tracking control was implemented for uncertain nonaffine systems. For some practical engineering systems, certain prescribed performance requirements such as convergence rate, or steady-state error need to be fulfilled.…”

Section: Introductionmentioning

confidence: 99%

Command filter‐based adaptive prescribed performance tracking control for uncertain pure‐feedback nonlinear systems with full‐state time‐varying constraints

Intl J Robust & Nonlinear

Zhang

2021

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This article is concerned with the issue of adaptive prescribed performance tracking control for a class of pure-feedback nonlinear systems with full-state time-varying constraints. By using the hyperbolic tangent function as nonlinear mapping technique, the obstacle of the full-state constraints is resolved. A novel adaptive control method is proposed by prescribed performance control with command filtered backstepping design, and the insufficient of the dynamic surface method is conquered by the error compensation mechanism. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, it is shown that all signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output tracking error always converges to the prescribed performance bound.Two simulation examples demonstrate the capability of the proposed control strategy.