Observer‐based nonlinear feedback decentralized neural adaptive dynamic surface control for large‐scale nonlinear systems
Observer‐based nonlinear feedback decentralized neural adaptive dynamic surface control for large‐scale nonlinear systems
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DOI：
10.1002/acs.2794
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发表时间：
2017-11
影响因子：
3.1
通讯作者：
Shigen Gao;Hai-rong Dong;B. Ning
中科院分区：
文献类型：
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作者：
Shigen Gao;Hai-rong Dong;B. Ning
针对一类状态不可测且子系统间存在不确定关联的非线性大系统，提出了一种基于观测器的分散神经自适应动态面控制的非线性增益反馈方法.在观测器的设计中使用神经网络来估计不可测的状态，从而方便控制器的设计。除了避免传统反推方法的复杂性问题外，新的非线性反馈增益方法赋予了自动调节能力，从而开创了动态面控制设计和动态性能的改善。设计了新的李雅普诺夫函数，并给出了严格的稳定性分析，证明了所有闭环信号保持半全局一致最终有界，且输出跟踪误差可以保证收敛到零附近的足够区域，且界值以显式的方式由设计参数表征.仿真和比较结果表明，以验证有效性。
This paper presents a nonlinear gain feedback technique for observer‐based decentralized neural adaptive dynamic surface control of a class of large‐scale nonlinear systems with immeasurable states and uncertain interconnections among subsystems. Neural networks are used in the observer design to estimate the immeasurable states and thus facilitate the control design. Besides avoiding the complexity problem in traditional backstepping, the new nonlinear feedback gain method endows an automatic regulation ability into the pioneering dynamic surface control design and improvement in dynamic performance. Novel Lyapunov function is designed and rigorous stability analysis is given to show that all the closed‐loop signals are kept semiglobally uniformly ultimately bounded, and the output tracking errors can be guaranteed to converge to sufficient area around zero, with the bound values characterized by design parameters in an explicit manner. Simulation and comparative results are shown to verify effectiveness.