Ke WANG, Huanliang XU, Zhaoyu ZHAI, Shougang REN, Haoyun WANG

Multiobjective distributed model predictive control method for facility environment control based on cooperative game theory

In order to achieve better control performance within the facility environment, this paper proposes a distributed model predictive control method, aiming at increasing the control precision of actuators, reducing energy consumption and equipment loss at the same time. Regarding optimizing this facility environment model, it could be treated as a multiobjective optimization problem. Referring to the cooperative game theory, each objective is regarded as a gamer. Each gamer considers both its own interests and other gamers' interests to achieve a balanced result when cooperation is achieved. The simulation experiments illustrate that the proposed control method is able to adjust the parameters of the facility environment to a preset interval properly. Moreover, in this speci c simulation environment, the proposed method achieves better performances than the single objective control method and traditional linear weighted multiobjective control method in most of the evaluation criteria.

PDF

___

[1] Wang HH, Wang Y, Delgado MS. The transition to modern agriculture: contract farming in developing economies. Am J Agr Econ 2014; 96: 1257-1271.
[2] Wang ZY, Qin LL, Wu G, Lu XT. Modeling of greenhouse temperature-humid system and model predictive control based on switching system control. Trans CSAE 2008; 24: 188-192.
[3] Flores-Alsina X, Amell M, Arnerlinck Y, Corominas L, Gernaey KV, Guo L, Lindblom E, Nopens I, Porro J, Shaw A et al. Balancing effluent quality, economic cost and greenhouse emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Sci Total Environ 2014; 466-467C: 616-624.
[4] Barrientos A, Cerro JD, Hernandez E. Game theory models for multi-robot patrolling of infrastructures. Int J Adv Robot Syst 2013; 10: 463-474.
[5] Shams F, Bacci G, Luise M. Energy-efficient power control for multiple-relay cooperative networks using Q-learning. IEEE T Wireless Commun 2015; 14: 1567-1580.
[6] Zhu ZL. Research and implementation on parameter identi cation of greenhouse mechanism model based on LF- PPSO. MSc, Nanjing Agricultural University, Nanjing, China, 2015.
[7] Yu QQ. Study on the greenhouse micro-climate model and intelligent control algorithm. MSc, Nanjing Agricultural University, Nanjing, China, 2015.
[8] Luo LH. Vehicle adaptive cruise control and the corresponding macroscopic traffic ow model. PhD, Zhejiang University, Hangzhou, China, 2011.
[9] Christo des PD, Scattolini R, Pe~na DM, Liu JF. Distributed model predictive control: a tutorial review and future research directions. Comput Chem Eng 2013; 51: 21-41.
[10] Armagan A, Dunson DB, Lee J. Generalized double pareto shrinkage. J Stat 2013; 23: 119-143.
[11] Zhen C, Jin S, Lai XM. Robust tolerance design method based on cooperative game. J Shanghai Jiaotong University 2011; 45: 1587-1592.
[12] Chen Z, Xie NG, Zhang ZM. Cooperative game method for structural optimization with multiple objectives. J Eng Mech-ASCE 2009; 26: 32-37.
[13] Huang Y, Xiao TT, Han P. An improved quantum particle swarm optimization and its application in system identi cation. Proc CSEE 2011; 31: 1132-1134.
[14] Chen ZR. The research and application of multi-objective optimization design method based on game theory. MSc, Zhejiang University, Hangzhou, China, 2013.
[15] Chen Q, Sun ZF. Energy-saving control strategy for greenhouse production based on crop temperature integration. Trans CSAE 2005; 21: 158-161.