Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control

• 成果类型: Article
• 署名作者: Bonalli, Riccardo; Lew, Thomas; Pavone, Marco
• 署名单位: Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Stanford University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3207865
• 发表日期: 2023
• 页码: 4570-4585
• 关键词: Algebraic/geometric methods; Constrained control; Nonlinear systems; optimal control; variational methods

摘要

Sequential convex programming (SCP) has recently gained significant popularity as an effective method for solving optimal control problems and has been successfully applied in several different domains. However, the theoretical analysis of SCP has received comparatively limited attention, and it is often restricted to discrete-time formulations. In this article, we present a unifying theoretical analysis of a fairly general class of SCP procedures for continuous-time optimal control problems. In addition to the derivation of convergence guarantees in a continuous-time setting, our analysis reveals two new numerical and practical insights. First, we show how one can more easily account for manifold-type constraints, which are a defining feature of optimal control of mechanical systems. Second, we show how our theoretical analysis can be leveraged to accelerate SCP-based optimal control methods by infusing techniques from indirect optimal control.