A Simultaneous Iterative Learning Control and Dynamic Modeling Approach for A Class of Nonlinear Systems

Abstract

This paper presents a novel simultaneous iterative learning control and dynamic modeling (SILCDM) approach. For a class of unknown and repeatable nonlinear discrete-time systems, a model-free iterative learning control (ILC) method is applied first. Meanwhile, by using the data generated during the repetitive operations, a novel iterative learning parameter estimation algorithm is proposed to calibrate the unknown time-varying parameter in the nonlinear system with known structure simultaneously. After the model parameter is well identified, the accurate dynamic model of the controlled plant is obtained. With this identified model, the model-free ILC method is then switched to a model-based optimal ILC method in order to get much better control performance. The theoretical analysis shows that the proposed pproach guarantees the convergence of both the system state and the parameter identification. The effectiveness of the SILCDM is further demonstrated through simulations.