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Asynchronous Control for Positive Markov Jump Systems
Kai Yin,Dedong Yang,Jiao Liu,Hongchao Li 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.2
A new result is provided for the asynchronous control analysis of positive Markov jump systems (PMJSs) in this paper. Firstly, a hidden Markov model is described to express the asynchronous circumstances that appear between the system modes and controller modes. Secondly, by utilizing a copositive stochastic Lyapunov function, a sufficient and necessary condition is given to guarantee the mean stability of PMJSs. Thirdly, we obtain another equivalent condition and design the corresponding asynchronous controller. Finally, the correctness of these results is verified by two numerical examples.
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A hybrid optimization strategy for simultaneous synthesis of heat exchanger network
Zhaoyi Huo,Hongchao Yin,Liang Zhao,Jianxiong Ye 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.10
The heat exchanger network synthesis problem often leads to large-scale non-convex mixed integer nonlinear programming formulations that contain many discrete and continuous variables, as well as nonlinear objective function or nonlinear constraints. In this paper, a novel method consisting of genetic algorithm and particle swarm optimization algorithm is proposed for simultaneous synthesis problem of heat exchanger networks. The simultaneous synthesis problem is solved in the following two levels: in the upper level, the network structures are generated randomly and reproduced using genetic algorithm; and in the lower level, heat load of units and stream-split heat flows are optimized through particle swarm optimization algorithm. The proposed approach is tested on four benchmark problems, and the obtained solutions are compared with those published in previous literature. The results of this study prove that the presented method is effective in obtaining the approximate optimal network with minimum total annual cost as performance index.
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