Suboptimal nonlinear model predictive control with input move-blocking

TL;DR

This paper introduces a novel approach to suboptimal nonlinear model predictive control that incorporates input move-blocking, improving control performance through a superimposed warm-start strategy and validated by numerical examples.

Contribution

It presents a new method combining input move-blocking with suboptimal MPC, including a warm-start technique for stepwise performance enhancement.

Findings

The proposed approach improves control performance.

Numerical example confirms theoretical benefits.

Superimposing warm-start enhances stability.

Abstract

This paper deals with the integration of input move-blocking into the framework of suboptimal model predictive control. The blocked input parameterization is explicitly considered as a source of suboptimality. A straightforward integration approach is to hold back a manually generated stabilizing fallback solution in some buffer for the case that the optimizer does not find a better input move-blocked solution. An extended approach superimposes the manually generated stabilizing warm-start by the move-blocked control sequence and enables a stepwise improvement of the control performance. In addition, this contribution provides a detailed review of the literature on input move-blocked model predictive control and combines important results with the findings of suboptimal model predictive control. A numerical example supports the theoretical results and shows the effectiveness of the proposed approach.