一类非线性切换系统的自适应神经动态面控制

Adaptive neural dynamic surface tracking control for a class of switched nonlinear systems

针对一类严格反馈型不确定非线性切换系统,提出了一种鲁棒自适应神经动态面跟踪控制方案.该方案在基于共同Lyapunov函数的后推法设计中引入动态面控制(dynamic surface control,DSC)技术,利用径向基神经网络逼近构造的未知共同上界函数,并将滤波器输出导数取代传统中间变量作为神经网络输入,降低了网络输入维度;同时利用Young’s不等式技术有效减少了神经网络控制器的可调参数数目.此外,理论证明了该控制方案可以保证在任意切换下闭环系统所有信号半全局一致终结有界,且跟踪误差在有限时间收敛到零的小邻域内.实验结果表明了所提方法达到了很好的跟踪性能.

An adaptive neural dynamic surface tracking control scheme is presented for a class of uncertain switched nonlinear systems in strict-feedback form under arbitrary switching. In this scheme, dynamic surface control(DSC) technology is introduced into backstepping design approach with common Lyapunov function method. The radial basis function neural network is adopted to approximate constructed unknown upper bound function, and with the help of DSC, the derivatives of filter output variables instead of traditional intermediate variables are taken as the neural network(NN) inputs. As a result, the dimension of NN inputs is reduced. Simultaneously, Yong's inequality is used to reduce the number of adjustable parameters of the control scheme. Moreover, it is proved that the proposed scheme is able to guarantee that all the signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded, with tracking error converging to a small neighborhood of zero by appropriately choosing design parameters. Simulation studies are carried out to illustrate the effectiveness of the proposed control.