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Title: An alternative method for the optimal switching problem of linear quadratic switched system
Authors: Xu, W
Feng, Z
Yiu, KFC 
Issue Date: Dec-2023
Source: Journal of industrial and management optimization, Dec. 2023, v. 19, no. 12, p. 8617-8632
Abstract: Optimal switching is a special class of optimal control problems for hybrid dynamic systems. In this paper, we consider the optimal switching problem of linear-quadratic switched systems. The aim is to design a suitable switching strategy with the constraint on the number of switchings so that the quadratical performance achieves the minimum value. This problem is difficult to be solved because of the tight coupling between the continuous switching time and the discrete switching sequence. In our method, we first divide this hybrid optimization problem into two subproblems. In each of them, only one type of variable is considered. Then, we develop a gradient-based method with the time-scaling transformation to process the optimal switching time problem and a branch and bound method based on a series of exact lower bounds to handle the optimal switching sequence problem, respectively. By solving these two subproblems alternatively, the optimal switching strategy satisfying the constraint on the number of switchings can be obtained. Numerical examples are given to demonstrate the efficiency of the proposed method.
Keywords: Branch and bound method
Lower bound analysis
Optimal switching
Switched system
Time-scaling transformation
Publisher: AIMS Press
Journal: Journal of industrial and management optimization 
ISSN: 1547-5816
EISSN: 1553-166X
DOI: 10.3934/jimo.2023054
Rights: JIMO is published by the American Institute of Mathematical Sciences and sponsored by Curtin University, Zhejiang University, and Chongqing Normal University. All rights reserved.
This article has been published in a revised form in Journal of Industrial and Management Optimization, https://doi.org/10.3934/jimo.2023054. This version is free to download for private research and study only. Not for redistribution, re-sale or use in derivative works.
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