陈学松(教授)

硕士生导师

所在单位:数学与统计学院

性别:男

在职信息:在职

学科:计算数学
运筹学与控制论
应用数学

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An approximating state-dependent control method based on modified pattern search optimization for nonlinear optimal control problem

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DOI码:10.1016/j.jfranklin.2024.106832

发表刊物:Journal of the Franklin Institute

关键字:Derivative free optimization; Nonlinear optimal control; Pattern search; State-dependent coefficient matrix; Time-varying linear optimal control

摘要:In this paper, an approximating state-dependent control (ASC) method with modified pattern search (MPS) optimization for nonlinear optimal control problem is proposed. First, by converting the nonlinear optimal control problem into a number of interrelated time-varying linear quadratic regulator subproblems, the ASC method can solve each subproblem iteratively until the approximate solution is obtained. Second, in each iterative control process, the MPS is used to solve the controllability optimization problem. The optimal state-dependent weighting coefficients are obtained during the MPS optimization. Moreover, the MPS uses simplex gradient to design the search direction, which makes the optimization process efficient and fast. The convergence of MPS optimization is also proved in this paper. Finally, two simulation examples are given to illustrate the effectiveness of ASC method using the MPS optimization. The result shows that the ASC method can reduce the iterations of the approximate solution, and the MPS optimization can optimize the control performance of the ASC method.

第一作者:Jianfeng Sun

论文类型:期刊论文

通讯作者:Xuesong Chen

卷号:361

期号:8

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发表时间:2024-05-15

收录刊物:SCI

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