周期时变时滞非线性参数化系统的自适应学习控制

针对一阶未知非线性参数化周期时变时滞系统, 设计了一种自适应学习控制方案. 假设未知时变参数, 时变时滞和参考信号的共同周期是已知的, 通过重构系统方程, 将包含时变时滞在内的所有未知时变项合并成为一个周期时变向量, 采用周期自适应律估计该向量. 通过构造一个Lyapunov-Krasovskii型复合能量函数证明了所有信号有界并且跟踪误差收敛. 结果被推广到一类含有混合参数的高阶非线性系统. 通过两个仿真例子说明本文所提出的控制算法的有效性.

Adaptive Learning Control for Nonlinearly Parameterized Systems with Periodically Time-varying Delays

An adaptive learning control scheme is designed for first-order nonlinearly parameterized systems with unknown pe- riodically time-varying delays.It is assumed that the common periodicity of unknown time-varying parameter,time-varying delay,and reference signal are known. By reconstructing the system equation,all unknown time-varying terms including the time-varying delay are combined into a periodically time-varying vector which is estimated by a periodic adaptation mechanism. By constructing a Lyapunov-Krasovskii-like composite energy function,we prove the boundedness of all signals and the con- vergence of tracking error.The results are extended to a class of high-order nonlinear systems with mixed parameters.Two simulation examples are provided to illustrate the effectiveness of the control algorithms proposed in this paper.