Robust adaptive backstepping control for a class of constrained non-affine nonlinear systems via self-organizing Hermite-polynomial-based neural network disturbance observer

被引:1
作者
Zhang, Qiang [1 ]
Wang, Cui [1 ]
机构
[1] Univ Jinan, Sch Elect Engn, Jinan 250022, Peoples R China
来源
ADVANCES IN MECHANICAL ENGINEERING | 2017年 / 9卷 / 05期
基金
中国国家自然科学基金;
关键词
Non-affine nonlinear system; input saturation; state constraint; self-organizing Hermite-polynomial-based neural network disturbance observer; robust control; fast-convergence nonlinear differentiator; backstepping; DYNAMIC SURFACE CONTROL; INPUT SATURATION; FEEDBACK LINEARIZATION; SYNCHRONIZATION; DESIGN;
D O I
10.1177/1687814017702811
中图分类号
O414.1 [热力学];
学科分类号
摘要
The article proposes a robust control approach based on self-organizing Hermite-polynomial-based neural network disturbance observer for a class of non-affine nonlinear systems with input saturation, state constraint, and unknown compound disturbance. Using Taylor series expansion, a hyperbolic tangent function, the non-affine nonlinear system with input saturation is transformed into time-varying affine system without input saturation, which can reduce step n+1 of the backstepping technique compared with conventional method. Next, a self-organizing Hermite-polynomial-based neural network disturbance observer is proposed to estimate the compound disturbance online. Then, the auxiliary systems are designed to solve state constraint for subsystems, and hyperbolic tangent function is used to approximate the saturated control input. Simulation results proved the effectiveness of the proposed control scheme.
引用
收藏
页数:12
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