Nonlinear Model Predictive Control Based on Constraint-Aware Particle Filtering/Smoothing

TL;DR

This paper introduces a novel constraint-aware particle filtering/smoothing approach for nonlinear model predictive control, offering an efficient alternative to traditional optimization-based methods, especially for complex nonlinear systems.

Contribution

It develops a sampling-based NMPC algorithm leveraging Bayesian estimation, reducing computational complexity and avoiding local minima issues common in conventional methods.

Findings

Efficient implementation for complex nonlinear systems

Potential mitigation of computational complexity issues

Successful validation through simulation studies

Abstract

Nonlinear model predictive control (NMPC) has gained widespread use in many applications. Its formulation traditionally involves repetitively solving a nonlinear constrained optimization problem online. In this paper, we investigate NMPC through the lens of Bayesian estimation and highlight that the Monte Carlo sampling method can offer a favorable way to implement NMPC. We develop a constraint-aware particle filtering/smoothing method and exploit it to implement NMPC. The new sampling-based NMPC algorithm can be executed easily and efficiently even for complex nonlinear systems, while potentially mitigating the issues of computational complexity and local minima faced by numerical optimization in conventional studies. The effectiveness of the proposed algorithm is evaluated through a simulation study.