Niu Ben, Wang Ding, Alotaibi Naif D, Alsaadi Fuad E
IEEE Trans Neural Netw Learn Syst. 2019 Apr;30(4):1076-1087. doi: 10.1109/TNNLS.2018.2860944. Epub 2018 Aug 20.
In this paper, the problem of adaptive neural state-feedback tracking control is considered for a class of stochastic nonstrict-feedback nonlinear switched systems with completely unknown nonlinearities. In the design procedure, the universal approximation capability of radial basis function neural networks is used for identifying the unknown compounded nonlinear functions, and a variable separation technique is employed to overcome the design difficulty caused by the nonstrict-feedback structure. The most outstanding novelty of this paper is that individual Lyapunov function of each subsystem is constructed by flexibly adopting the upper and lower bounds of the control gain functions of each subsystem. Furthermore, by combining the average dwell-time scheme and the adaptive backstepping design, a valid adaptive neural state-feedback controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. Finally, the availability of the developed control scheme is verified by two simulation examples.
本文研究了一类具有完全未知非线性的随机非严格反馈非线性切换系统的自适应神经状态反馈跟踪控制问题。在设计过程中，利用径向基函数神经网络的通用逼近能力来辨识未知的复合非线性函数，并采用变量分离技术克服非严格反馈结构带来的设计困难。本文最突出的新颖之处在于，通过灵活采用各子系统控制增益函数的上下界来构造各子系统的个体李雅普诺夫函数。此外，结合平均驻留时间方案和自适应反步设计，提出了一种有效的自适应神经状态反馈控制器设计算法，使得切换闭环系统的所有信号在概率意义下全局一致最终有界，且跟踪误差最终以概率收敛到原点的一个小邻域内。最后，通过两个仿真例子验证了所提出控制方案的有效性。
