A Convex Optimization Approach to Chance-Constrained Linear Stochastic Drift Counteraction Optimal Control

In this paper, we propose a convex optimization approach to chance-constrained drift counteraction optimal control (DCOC) problems for linear systems with additive stochastic disturbances. Chance-constrained DCOC aims to compute an optimal control law to maximize the time duration before the probabi...

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Bibliographic Details
Published in2021 60th IEEE Conference on Decision and Control (CDC) pp. 898 - 903
Main Authors Tang, Sunbochen, Li, Nan, Kolmanovsky, Ilya, Zidek, Robert
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.12.2021
Subjects
Additives
Computational efficiency
Convex functions
Linear systems
Optimal control
Programming
Stochastic processes
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Summary:In this paper, we propose a convex optimization approach to chance-constrained drift counteraction optimal control (DCOC) problems for linear systems with additive stochastic disturbances. Chance-constrained DCOC aims to compute an optimal control law to maximize the time duration before the probability of violating a prescribed set of constraints can no longer be maintained to be below a specified risk level. While conventional approaches to this problem involve solving a mixed-integer programming problem, we show that an optimal solution to the problem can also be found by solving a convex second-order cone programming problem without integer variables. We illustrate the application of chance-constrained DCOC to an automotive adaptive cruise control example.
ISSN:2576-2370
DOI:10.1109/CDC45484.2021.9683303