An efficient algorithm for collaborative learning model predictive control of nonlinear systems

Yanze Liu; Dong Shen

doi:10.1016/j.isatra.2021.03.039

An efficient algorithm for collaborative learning model predictive control of nonlinear systems

ISA Trans. 2022 Feb:121:1-10. doi: 10.1016/j.isatra.2021.03.039. Epub 2021 Mar 31.

Authors

Yanze Liu¹, Dong Shen²

Affiliations

¹ Beijing University of Chemical Technology, Chaoyang District 100029, Beijing, China. Electronic address: yzliu2@mail.buct.edu.cn.
² Renmin University of China, Haidian District 100872, Beijing, China. Electronic address: dshen@ieee.org.

PMID: 33845999
DOI: 10.1016/j.isatra.2021.03.039

Abstract

This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of systems. These subsystems work together to find the optimal trajectory under given constraints in this study. We implement the collaboration idea into the learning model predictive control framework and reduce the computational burden by modifying the barycentric function. The properties, including recursive feasibility, stability, convergence, and optimality, are proved. The simulation is presented to show the system performance with the proposed collaborative learning model predictive control strategy.

Keywords: Collaborative control; Data-driven control; Iterative learning control; Model predictive control.