Iterative learning control with inverse model updated with quadratic optimization

This paper discusses a new iterative learning control method which employs inverse model of the system updated every iteration with quadratic optimization. The proposed method constructs an inverse model without estimating forward model, and the inverse model is described by finite impulse response model. These points enable the proposed method to guarantee the convergence and monotonic improvement of the inverse model. A numerical example and a practical experiment with a motor are shown to demonstrate the effectiveness of the proposed method.