Iterative Learning Economic Model Predictive Control

TL;DR

This paper introduces an iterative learning economic MPC that can handle various control objectives without requiring initial convergence, improving performance through learning from previous trajectories.

Contribution

It extends economic MPC to allow non-convergent initial trajectories and guarantees performance improvement by learning from past trajectories.

Findings

Ensures recursive feasibility under standard MPC assumptions.

Guarantees convergence for stabilization tasks with convergent initial trajectories.

Demonstrates effectiveness across different control tasks and systems.

Abstract

An iterative learning based economic model predictive controller (ILEMPC) is proposed for repetitive tasks in this paper. Compared with existing works, the initial feasible trajectory of the proposed ILEMPC is not restricted to be convergent to an equilibrium so it can handle various types of control objectives: stabilization, tracking a periodic trajectory and even pure economic optimization. The controller can learn from the previous closed-loop trajectory, resulting in a performance which is guaranteed to be no worse than the previous one. Under some standard assumptions in model predictive control, we show that recursive feasibility is ensured. Furthermore, for stabilization problem, the convergence of each learned trajectory and the learning process are established provided the initial trajectory is convergent. Numerical examples show that the proposed control strategy works well for different types of control tasks and systems.