Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

NIU Ben, WANG Xiaoan, WANG Xiaomei, WANG Xinjun, LI Tao

系统科学与复杂性(英文) ›› 2024, Vol. 37 ›› Issue (3) : 965-984.

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系统科学与复杂性(英文) ›› 2024, Vol. 37 ›› Issue (3) : 965-984. DOI: 10.1007/s11424-024-1259-8

Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

    NIU Ben1,2, WANG Xiaoan3, WANG Xiaomei2, WANG Xinjun2, LI Tao4
作者信息 +

Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance

    NIU Ben1,2, WANG Xiaoan3, WANG Xiaomei2, WANG Xinjun2, LI Tao4
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文章历史 +

摘要

In this paper, the authors propose an adaptive Barrier-Lyapunov-Functions (BLFs) based control scheme for nonlinear pure-feedback systems with full state constraints. Due to the coexist of the non-affine structure and full state constraints, it is very difficult to construct a desired controller for the considered system. According to the mean value theorem, the authors transform the pure-feedback system into a system with strict-feedback structure, so that the well-known backstepping method can be applied. Then, in the backstepping design process, the BLFs are employed to avoid the violation of the state constraints, and neural networks (NNs) are directly used to online approximate the unknown packaged nonlinear terms. The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Meanwhile, it is shown that the constraint requirement on the system will not be violated during the operation. Finally, two simulation examples are provided to show the effectiveness of the proposed control scheme.

Abstract

In this paper, the authors propose an adaptive Barrier-Lyapunov-Functions (BLFs) based control scheme for nonlinear pure-feedback systems with full state constraints. Due to the coexist of the non-affine structure and full state constraints, it is very difficult to construct a desired controller for the considered system. According to the mean value theorem, the authors transform the pure-feedback system into a system with strict-feedback structure, so that the well-known backstepping method can be applied. Then, in the backstepping design process, the BLFs are employed to avoid the violation of the state constraints, and neural networks (NNs) are directly used to online approximate the unknown packaged nonlinear terms. The presented controller ensures that all the signals in the closed-loop system are bounded and the tracking error asymptotically converges to zero. Meanwhile, it is shown that the constraint requirement on the system will not be violated during the operation. Finally, two simulation examples are provided to show the effectiveness of the proposed control scheme.

关键词

Asymptotic tracking control / barrier Lyapunov functions / full state constraints / nonlinear pure-feedback systems

Key words

Asymptotic tracking control / barrier Lyapunov functions / full state constraints / nonlinear pure-feedback systems

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NIU Ben , WANG Xiaoan , WANG Xiaomei , WANG Xinjun , LI Tao. Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance. 系统科学与复杂性(英文), 2024, 37(3): 965-984 https://doi.org/10.1007/s11424-024-1259-8
NIU Ben , WANG Xiaoan , WANG Xiaomei , WANG Xinjun , LI Tao. Adaptive Barrier-Lyapunov-Functions Based Control Scheme of Nonlinear Pure-Feedback Systems with Full State Constraints and Asymptotic Tracking Performance. Journal of Systems Science and Complexity, 2024, 37(3): 965-984 https://doi.org/10.1007/s11424-024-1259-8

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基金

This research was supported in part by the National Natural Science Foundation of China under Grant No. 62303278, and in part by the Taishan Scholar Project of Shandong Province of China under Grant No. tsqn201909078.
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