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

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...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 68; no. 8; pp. 1 - 16
Main Authors: Bonalli, Riccardo, Lew, Thomas, Pavone, Marco
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algebraic/geometric methods
Computer Science
Constrained control
Control methods
Convergence
Convex functions
Convexity
Costs
Mathematics
Mechanical systems
Nonlinear systems
Optimal control
Optimization and Control
Programming
Systems and Control
Time optimal control
Time setting
Trajectory
Urban areas
Variational methods
ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary: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 paper, 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.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2022.3207865