A penalty function-based random search algorithm for optimal control of switched systems with stochastic constraints and its application in automobile test-driving with gear shifts. (August 2022)

• Record Type: Journal Article
• Authors: Wu, Xiang; Lin, Jinxing; Zhang, Kanjian; Cheng, Ming
• Is Part Of: Nonlinear analysis. Volume 45(2022)
• Publication Date: 2022-08
• Subjects: Optimal control -- Switched system -- Stochastic constraints -- A PFRS algorithm -- Automobile test-driving; Nonlinear functional analysis -- Periodicals; Analyse fonctionnelle non linéaire -- Périodiques; Nonlinear functional analysis; Periodicals; 515.7248
• DOI: 10.1016/j.nahs.2022.101218
• Languages: English
• ISSNs: 1751-570X

Abstract

Abstract: Practical industrial process is usually a dynamic process including uncertainty. Stochastic constraints can be used for industrial process modeling, when system sate and/or control input constraints cannot be strictly satisfied. Thus, optimal control of switched systems with stochastic constraints can be available to address practical industrial process problems with different modes. In general, obtaining an analytical solution of the optimal control problem is usually very difficult due to the discrete nature of the switching law and the complexity of stochastic constraints. To obtain a numerical solution, this problem is formulated as a constrained nonlinear parameter selection problem (CNPSP) based on a relaxation transformation (RT) technique, an adaptive sample approximation (ASA) method, a smooth approximation (SA) technique, and a control parameterization (CP) method. Following that, a penalty function-based random search (PFRS) algorithm is designed for solving the CNPSP based on a novel search rule-based penalty function (NSRPF) method and a novel random search (NRS) algorithm. The convergence results show that the proposed method is globally convergent. Finally, an optimal control problem in automobile test-driving with gear shifts (ATGS) is further extended to illustrate the effectiveness of the proposed method by taking into account some stochastic constraints. Numerical results show that compared with other typical methods, the proposed method is less conservative and can obtain a stable and robust performance when considering the small perturbations in initial system state. In addition, to balance the computation amount and the numerical solution accuracy, a tolerance setting method is also provided by the numerical analysis technique. Highlights: A RT technique is used for relaxing integer variables into continuous variables. An ASA method is proposed for handling stochastic constraints. A PFRS algorithm is designed for solving the resulting CNPSP. The convergence results for the PFRS algorithm are established. A problem in ATGS is used to illustrate the effectiveness of the proposed method.