Data-driven predictive iterative learning control for a class of multiple-input and multiple-output nonlinear systems

Abstract

This paper presents a novel data-driven predictive iterative learning control (DDPILC) scheme based on a new dynamic linearization technique along the iteration axis for a class of repeatable multiple-input and multiple-output nonlinear discrete-time systems. The proposed DDPILC scheme combines iterative learning control with predictive control and the distinct feature of the scheme is that the controller design depends only on the measured input/output data without using any model information of the controlled plant. In addition, if the control system is subjected to input and output constraints, the constrained DDPILC scheme is also proposed. The theoretical analysis shows that with random initial operation conditions, the proposed unconstrained DDPILC scheme guarantees monotonic and pointwise convergence while the constrained DDPILC scheme guarantees the asymptotic and pointwise convergence. The applicability and effectiveness of the proposed DDPILC schemes are further verified through simulations.