Real-Time Sequential Convex Programming for Optimal Control Applications

TL;DR

This paper introduces RTSCP, a real-time method for solving sequences of nonlinear optimization problems with applications in model predictive control, providing convergence guarantees and demonstrating practical effectiveness.

Contribution

The paper presents RTSCP, a novel real-time sequential convex programming approach with a new proof of local convergence and applicability to nonlinear model predictive control.

Findings

RTSCP effectively solves nonlinear optimization problems in real-time.

The method provides a contraction estimate ensuring convergence.

Application to nonlinear model predictive control demonstrates practical utility.

Abstract

This paper proposes real-time sequential convex programming (RTSCP), a method for solving a sequence of nonlinear optimization problems depending on an online parameter. We provide a contraction estimate for the proposed method and, as a byproduct, a new proof of the local convergence of sequential convex programming. The approach is illustrated by an example where RTSCP is applied to nonlinear model predictive control.