Accelerated Nonlinear Model Predictive Control by Exploiting Saturation

TL;DR

This paper introduces a method to speed up nonlinear model predictive control by reusing saturated control signals across multiple steps, reducing the number of complex optimizations needed while maintaining control performance.

Contribution

The paper proposes a novel approach that exploits saturation in control inputs to avoid unnecessary optimization problems, enhancing computational efficiency in NMPC.

Findings

Significant reduction in the number of NLPs solved during control.

Maintains control performance despite fewer optimizations.

Ensures timely reactivation of NMPC for safe system control.

Abstract

We present an approach for accelerating nonlinear model predictive control. If the current optimal input signal is saturated, also the optimal signals in subsequent time steps often are. We propose to use the open-loop optimal input signals whenever the first and some subsequent input signals are saturated. We only solve the next optimal control problem, when a non-saturated signal is encountered, or the end of the horizon is reached. In this way, we can save a significant number of NLPs to be solved while on the other hand keep the performance loss small. Furthermore, the NMPC is reactivated in time when it comes to controlling the system safely to its reference.