自动化学报2017，Vol.43Issue(8)：1383-1392,10.DOI:10.16383/j.aas.2017.c160372
输入饱和的一类切换系统神经网络跟踪控制
Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation
摘要
Abstract
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 ar-bitrary 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 approx-imated. 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.关键词
切换非线性系统/公共的Lyapunov函数/非对称饱和/自适应BacksteppingKey words
Switched nonlinear systems/common Lyapunov function/asymmetric saturation/adaptive backstepping引用本文复制引用
司文杰,董训德,王聪..输入饱和的一类切换系统神经网络跟踪控制[J].自动化学报,2017,43(8):1383-1392,10.基金项目
国家重大科研仪器研制项目(61527811) 资助Supported by National Research and Development Program for Major Research Instruments (61527811) （61527811）
