Data-driven model predictive control design for offset-free tracking of nonlinear systems

• Published in: International journal of control Vol. 96; no. 6; pp. 1408 - 1423
• Main Authors: Park, Byungjun, Kim, Jong Woo, Lee, Jong Min
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.06.2023; Taylor & Francis Ltd
• Subjects: Continuously stirred tank reactors; differential dynamic programming; Dynamic programming; gap metric; Initial conditions; linear time-varying model predictive control; Model predictive control; Nonlinear systems; Predictive control; Tracking control; Trajectory optimization
• ISSN: 0020-7179; 1366-5820
• DOI: 10.1080/00207179.2022.2051074

We propose a design of data-driven Model Predictive Control (MPC) using a suboptimal trajectory and the linear time-varying (LTV) models from data-driven trajectory optimisation that achieves offset-free tracking. Data-driven constrained differential dynamic programming (CDDP) is exploited to improve the trajectory iteratively without the knowledge of the nonlinear model. A trajectory is divided to the transient and steady state regions, controlled by the Linear time-varying MPC (LTVMPC) and the offset-free linear MPC (LMPC), respectively. We prove the feasibility of the proposed LTVMPC in the transient region, and the offset-free tracking property of LMPC. The proposed scheme is validated to a continuous stirred tank reactor (CSTR) process. Simulation studies show that the suboptimal trajectory and LTV models are generated by CDDP, and the proposed MPC achieves offset-free tracking and disturbance rejection for a set of initial conditions and set points in the operating region.