Iterative learning for trajectory control

Abstract

Learning control is an iterative approach to the problem of improving transient behavior for processes that are repetitive in nature. A complete analysis of the learning control problem is given for the case of linear, time-invariant plants and controllers. The analysis offers insights into the nature of the solution of learning control schemes. First, an approach based on parameter estimation is given. Then, it is shown that for finite-horizon problems it is possible to design a learning control algorithm which converges in one step. A brief simulation example is presented to illustrate the effectiveness of iterative learning for controlling the trajectory of a nonlinear robot manipulator.<>