Binary-Valued Observation Based Data-driven Iterative Learning Control

Abstract:In this paper, a binary-valued observation-based data-driven iterative learning control(BVO-DDILC) scheme is proposed. An equivalent dynamic linearization method is used to transform the discrete-time nonlinear system to a linear data model. Note that the output of the system is measured by a binary-valued sensor. With the help of linear data model, the threshold value of binary-valued sensor is designed to be adjustable. On this basis, the parameter estimation law and control law with binary-valued observation are designed respectively. Strict mathematical analysis shows that the proposed BVODDILC algorithm can guarantee the convergence of parameter estimation in the finite time interval along the iteration axis, and the tracking error is also asymptotically convergent. A numerical example is given to demonstrate the effectiveness of the proposed method.

Translation or Not:no