具有输入及状态未建模动态系统的输出反馈自适应控制

针对一类具有输入及状态未建模动态的非线性系统, 设计K滤波器来估计系统不可量测状态, 基于动态面控制技术并利用径向基函数神经网络的逼近能力, 提出一种输出反馈自适应跟踪控制方案. 利用Nussbaum 函数性质, 有效地解决了高频增益符号未知问题. 在控制器设计中引入规范化信号来约束输入未建模动态, 从而有效地抑制其产生的扰动. 通过理论分析证明了闭环控制系统是半全局一致终结有界的.

Output feedback adaptive control of systems with input and state unmodeled dynamics

Based on the dynamic surface control(DSC) technique, and by using the approximation capability of radial basis function(RBF) neural networks, an output feedback adaptive tracking control scheme is proposed for a class of nonlinear systems with input and state unmodeled dynamics. K-filters are designed to estimate the unmeasured states. By using the properties of the Nussbaum type function, the problem of the unknown high frequency gain sign is effectively solved. A normalization signal is designed to restrict the input unmodeled dynamics, and the disturbance caused by it is effectively suppressed. By theoretical analysis, it is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded.