A data-driven terminal iterative learning control for nonlinear discrete-time systems

This paper presents a new data-driven optimal terminal iterative learning control (TILC) using time-varying control input signals to enhance control performance. The iterative learning control input is updated using the terminal output in previous runs, together with the control input information in previous runs and previous time instants of the current run, without the need of any reference trajectory. The proposed approach is data-driven and only the boundedness of partial derivatives of the nonlinear system with respect to control inputs is assumed for the control system design and analysis. The simulation results illustrate the applicability and effectiveness of the proposed approach.