Efficient iterative learning model predictive control for uncertain nonlinear discrete-time systems

This work focuses on the iterative learning model predictive control (ILMPC) design for nonlinear discrete-time batch systems. Different from the existing results, a novel efficient two-dimensional (2D) ILMPC approach is firstly proposed based on the 2-D system theory, which is able to guarantee the H tracking performance with lower computation load. Furthermore, based on the newly established event-triggered mechanisms, an event-triggered 2-D ILMPC is developed to reduce the occupation of the network resources while ensuring the Htracking performance. For the proposed ILMPC schemes, the sufficient conditions for the Htracking performance are provided explicitly by employing the linear matrix inequalities (LMI) techniques. Finally, the effectiveness of the proposed ILMPC strategies are demonstrated through numerical simulations. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.