ABSTRACT
The optimal solution of multi-objective control problem (MOCP) isn't unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users' preference information plays a key role in control performance. In this paper, the feedback control law and users' preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.
- Lihong Xu, Bingkun Zhu, Controller Design of Conflict Multi-objective UKACC 2008, England.Google Scholar
- Lihong Xu, Zhiqiang Zou, and Qingsong Hu, Two-Layer Optimization Compatible Control for Multi-Objective Control Systems, IEEE International Conference on Networking, Sensing and Control, 23--25 April, 2006, pp.658--663.Google Scholar
- Lihong Xu, Qingsong Hu and Erik Goodman. Two layer Iterative Multi-objective Compatible Control algorithm. the 46th IEEE Conference on Decision and Control,2007, pp2992--2997.Google Scholar
- Masaaki, I. (1997), Multi-objective optimal control through linear programming with interval objective function, SICE, July 29--31, Tokushima, pp. 1185--1188.Google Scholar
- Rangan, S. & Poolla, K.(1997), Weighted optimization for multi-objective full-information control problems, System & Control Letter, Vol.31, pp. 207--213. Google ScholarDigital Library
- Eisenhart, K. J. (2003), Multi-objective optimal control problems with endpoint and state constraint, Ph.D. thesis, Western Michigan University.Google Scholar
- Iskander M. G.(2003), Using different dominance criteria in stochastic fuzzy linear multi-objective programming: A case of fuzzy weighted objective function, Mathematical and Computer Modeling, Vol. 37,pp. 67--176. Google ScholarDigital Library
- Zhao, Z., Yasuyuki, I. & Masatoshi, N.(2003), Diffuser multi-objective control for STEC plant, IEEE Conference on Systems, Man and Cybernetics, October 5--8, 2003, pp. 148--153.Google Scholar
- Masaaki, I. (1997), Multi-objective optimal control through linear programming with interval objective function, SICE, July 29--31, Tokushima, pp. 1185--1188.Google Scholar
- Scherer,C.W.(1995), Multi-objective Control, IEEE Transaction on Automatic Control, vol. 40, pp. 1054--1061.Google ScholarCross Ref
- Scherer, C, Gahinet P. & Chilali, M.(1997), Multi-objective output feedback control via LMI Optimization, IEEE Transactions on Automatic Control, vol. 42, pp. 896--911.Google ScholarCross Ref
- Kalyanmoy Deb (2002), Multi-Objective Optimization using Evolutionary Algorithms, JOHN WILEY & SONS, LTD, pp. 235--237Google Scholar
Index Terms
- An improved MOCC with feedback control structure based on preference
Recommendations
A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm
When solving multiobjective optimization problems, preference-based evolutionary multiobjective optimization (EMO) algorithms introduce preference information into an evolutionary algorithm in order to focus the search for objective vectors towards the ...
Fuzzy preference-based multi-objective optimization method
Multiobjective evolutionary computation is still quite young and there are many open research problems. This paper is an attempt to design a hybridized Multiobjective Evolutionary Optimization Algorithm with fuzzy logic called Fuzzy Preference-Based ...
A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic
A multi-objective optimization evolutionary algorithm incorporating preference information interactively is proposed. A new nine grade evaluation method is used to quantify the linguistic preferences expressed by the decision maker (DM) so as to reduce ...
Comments