A Data-driven Iterative Learning Control for I/O Constrained Nonlinear Systems

Key Words:iterative learning control; data-driven control; I/O constraints; nonlinear discrete systems

Abstract:In this paper, a new data-driven method is proposed for I/O constrained nonlinear systems. An iterative dynamic linearization is introduced for the controlled nonlinear systems. All of the constraints on the system inputs and outputs are reformulated with a linear matrix inequality. The learning control law is then developed by minimizing a predesigned cost function subjected to the linear matrix inequality constraint. Simulation results illustrate the effectiveness of the proposed approach.

Translation or Not:no