一类线性时变系统组合自适应迭代学习控制

针对一类有限时间区间上具有可重复性的BIBO稳定的一阶线性时变系统,将模型参考自适应控制方法与迭代学习相结合,提出了组合模型参考自适应迭代学习控制算法.基于Lyapunov方法推导出迭代学习控制律以及针对时变惯性参数与时不变高频增益的组合自适应参数更新律.该算法适于控制快时变系统,并使跟踪误差、参数估计误差和控制信号有界.当迭代次数趋于无穷时,跟踪误差关于有限时间区间一致收敛到零.系统仿真验证了所提控制算法的有效性.

Combined Adaptive Iterative Learning Control for a Class of Linear Time-varying Systems

With the combination of model reference adaptive control and iterative learning control,a combined model reference adaptive iterative learning control algorithm was proposed for a class of first order linear time-varying systems which are BIBO stable and repeatable in a finite time interval. By means of Lyapunov technique,an iterative learning control law with a combined adaptive update law for time-varying inertial parameter and time-invariant high frequency gain was derived. The approach is suitable to control the systems with rapidly time-varying parameters. The boundedness can be guaranteed for tracking error,parameter errors and control signal. When the number of iteration trends to infinite,the tracking error will converge to zero uniformly with respect to the finite time interval. The effectiveness of the algorithm is demonstrated by simulations.