A low-cost pole-placement MPC algorithm for controlling complex dynamic systems. (March 2022) - Zhang, Zhiming; Xie, Lei; Lu, Shan; Rossiter, John Anthony; Su, Hongye
Abstract: Due to the ability to handle constraints systematically and predict system evolution with models, model predictive control (MPC) methods have been widely studied and implemented in many industries. At the same time, low-cost MPC has received widespread attention due to its simple principle and easy implementation. This paper proposes a new low-cost MPC method and uses this method to put forward new insights on the performance improvement of complex dynamic system control. Firstly, using the concept of preprocessing, a novel MPC prediction structure is proposed under the independent model mode. Then through rigorous proofs, the properties of the proposed MPC algorithm are analyzed. Finally, through the study of three industrial cases, the proposal's deployment procedure and efficacy are illustrated in detail. Compared with other low-cost predictive control algorithms, the effectiveness of the method has been presented. Highlights: A novel prediction structure design. Pole-placement MPC with better performance and low computational cost. Desired closed-loop dynamics can be designed without tedious tuning. The rigorous analysis and pseudocode of the proposed algorithm are given.