Adaptive iterative learning control based on characteristic model

Modeling the real system is difficult, in order to solve this problem, a method called characteristic modeling is used to solve a class of nonlinear system. A least squares iterative identification method with variant forgetting factors are used to obtain the unknown parameters in the model, this method is able to reduce the identification error. An optimal controller and an adaptive controller are used to control the characteristic model. Simulation results illustrated that the characteristic model can describe the real system effectively by using the least squares iterative identification method. The adaptive controller proposed in this work can achieve a lower tracing error than the optimal controller.