输入饱和的一类切换系统神经网络跟踪控制

针对单输入单输出系统研究一种在任意切换下的跟踪控制问题，系统包含未知扰动和输入饱和特性.首先，利用高斯误差函数描述一个连续可导的非对称饱和模型.其次，利用径向基神经网络（Radial basis function neural network，RBF NN）逼近未知的系统动态.最后，基于公共的Lyapunov函数构造状态反馈控制器.设计的控制器避免过多参数调节从而减轻计算负荷.结果展示本文给出的状态反馈控制器可以保证闭环系统的所有信号是半全局一致有界的，并且跟踪误差可收敛到零值小的领域内.最后的仿真结果进一步验证提出方法的有效性.

Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation

This paper deals with tracking control for a class of single input and single output (SISO) uncertain strict-feedback switched nonlinear systems with input asymmetric saturation actuator, unknown external disturbance and arbitrary switchings. Firstly, Gaussian error function is employed to represent a novel continuous differentiable asymmetric saturation model. Secondly, by employing radial basis function neural network (RBF NN), unknown functions are approximated. At last, a state-feedback controller is constructed by using common Lyapunov function method. The designed controller decreases the number of learning parameters, thus reduces the computational burden. The designed state-feedback controller is shown to be able to guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two simulation examples are presented to show the effectiveness of the proposed approach.