Error trajectory tracking by robust learning control for nonlinear systems
一类非线性系统的误差轨迹跟踪鲁棒学习控制算法

For a class of nonlinear systems with non-parametric uncertainties, we present a robust iterative learning control algorithm, in which the iterative initial value can be arbitrary, relaxing the initial conditions required in conventional methods. The learning controller is designed based on the Lyapunov-like synthesis, compensating uncertainties by robust limited-magnitude learning mechanism, and the error can follow its desired trajectory accurately in the entire time interval. The desired error trajectory with attenuation traits is predetermined by the initial error value at the beginning of the iteration. The error converges to the neighborhood of the origin after the predetermined time, and the radius of the neighborhood can be as small as required. The effectiveness of the proposed method is proven by theoretical analysis and verification results.