A Gauss-Newton-Like Hessian Approximation for Economic NMPC

TL;DR

This paper introduces a new positive-definite Hessian approximation for Economic NMPC that enhances computational efficiency, enabling real-time implementation without sacrificing convergence speed, demonstrated through simulation examples.

Contribution

It proposes a Gauss-Newton-like Hessian approximation tailored for real-time EMPC, improving computational performance for constrained nonlinear systems.

Findings

Effective in simulation examples

Enables real-time EMPC implementation

Maintains fast convergence with the new Hessian approximation

Abstract

Economic Model Predictive Control (EMPC) has recently become popular because of its ability to control constrained nonlinear systems while explicitly optimizing a prescribed performance criterion. Large performance gains have been reported for many applications and closed-loop stability has been recently investigated. However, computational performance still remains an open issue and only few contributions have proposed real-time algorithms tailored to EMPC. We perform a step towards computationally cheap algorithms for EMPC by proposing a new positive-definite Hessian approximation which does not hinder fast convergence and is suitable for being used within the real-time iteration (RTI) scheme. We provide two simulation examples to demonstrate the effectiveness of RTI-based EMPC relying on the proposed Hessian approximation.