Model predictive control with dynamic move blocking

TL;DR

This paper introduces a dynamic move blocking strategy for Model Predictive Control that leverages previous solutions, ensuring stability and bounding performance loss compared to optimal solutions.

Contribution

It proposes a novel dynamic move blocking method that improves MPC efficiency while maintaining stability and providing theoretical performance bounds.

Findings

The approach preserves asymptotic stability.

Performance degradation is theoretically bounded.

The method exploits previous solutions for efficiency.

Abstract

Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints. Nonetheless, in many applications, a major challenge arises, that is finding the optimal solution within a single sampling instant to apply a receding-horizon policy. In such cases, many suboptimal solutions have been proposed, among which the possibility of "blocking" some moves a-priori. In this paper, we propose a dynamic approach to move blocking, to exploit the solution already available at the previous iteration, and we show not only that such an approach preserves asymptotic stability, but also that the decrease of performance with respect to the ideal solution can be theoretically bounded.