一类时变系统模型参考自适应迭代学习控制

针对一类有限时间区间上可重复运行的有界输入有界输出稳定的一阶线性时变系统,其高频增益和惯性参数均时变,为使之能够跟踪不同的参考轨迹,将模型参考自适应控制方法与迭代学习方法相结合,提出了模型参考自适应迭代学习控制算法.基于类李雅普诺夫(Lyapunov-like)函数证明了当迭代次数趋于无穷时,跟踪误差在有限时间区间上一致收敛到零,并证明了闭环系统中参数估计和控制信号有界.系统仿真验证了所提控制算法的有效性.

Model Reference Adaptive Iterative Learning Control for a Class of Time-varying Systems

A model reference adaptive iterative learning control algorithm is proposed for a class of first order linear time-varying systems with time-varying high-frequency-gain and inertial parameter in which the boundary input and the boundary output are stable and repeatable in a finite time interval. The model reference adaptive control is combined with iterative learning control. The approach adapts the systems with time-varying uncertain parameters for tracing different reference trajectories. Based on Lyapunov-like function it is proved that the output tracking error converges to zero uniformly in the finite time interval when the trail approaches to infinity. The boundedness of the estimated parameters and the control input in the closed system is proved. The effectiveness of the algorithm is demonstrated by simulations.