An efficient algorithm for collaborative learning model predictive control of nonlinear systems. (February 2022) - Liu, Yanze
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. Highlights: A collaborative learning model predictive control for nonlinear systems. An computation efficient algorithm for nonlinear systems. The overall system can improve its performance by trial learning. Multiple subsystems work independently but to complete a give control task jointly.