Periodic Disturbance Learning Model Predictive Control

Periodic Disturbance Learning Model Predictive Control Syed Hassan Ahmed , Tommaso Bonetti, and Lorenzo Fagiano Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy E-mail: [email protected] DOI: 10.1109/LCSYS.2025.3586633 Journal Paper IEEE CONTROL SYSTEMS LETTERS, VOL. 9, pp. 1826-1831, 2025 Abstract A novel Model Predictive Control (MPC) framework called disturbance-learning MPC (DLMPC) for constrained LTI systems subject to bounded disturbances is proposed. The primary objective is to improve the disturbance rejection performance of the tube-based MPC (tubeMPC) law, especially focusing on periodic disturbance signals. Based on convex optimization, the method uses real-time measurements to learn a model of the disturbance, to predict its future behavior. By including this model in the MPC, the latter can proactively counteract the disturbance, significantly improving closed-loop performance. The presented technique includes the disturbance model while preserving robust recursive feasibility and constraint satisfaction. The effectiveness of DL-MPC is demonstrated through simulation of a multivariable nonlinear system, a Continuous-flow Stirred Tank Reactor, subject to periodic disturbances. The results clearly show enhanced tracking accuracy compared to nominal MPC and tube-MPC methods.