CONTINUOUS-TIME SYSTEMS IDENTIFICATION BASED ON ITERATIVE LEARNING CONTROL

The paper proposes a novel approach to identification of continuous-time systems from sampled I/O data. The coefficients of plant transfer functions are directly identified by applying an iterative learning control which enables us to achieve perfect tracking for uncertain plants by iteration of trials. Furthermore, one way to make the method robust against the measurement noises is shown. One of the merits of the proposed method is that it does not require time-derivative of I/O signals. In addition, it indicates us the estimation accuracy explicitly through tracking control experiments. Numerical examples are given to illustrate the effectiveness of the proposed method.