The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist a user to select an appropriate controller to implement is a postprocessing issue. In this paper, we develop a method of cluster analysis of the Pareto optimal designs to discover the similarity of the optimal controllers. After we identify the clusters of optimal controllers, we develop a switching strategy to select controls from different clusters to improve the performance. Numerical and experimental results show that the switching control algorithm is quite promising.
Cluster Analysis and Switching Algorithm of Multi-Objective Optimal Control Design
Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received March 7, 2016; final manuscript received August 12, 2016; published online September 30, 2016. Assoc. Editor: Lei Zuo.
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Qin, Z., and Sun, J. (September 30, 2016). "Cluster Analysis and Switching Algorithm of Multi-Objective Optimal Control Design." ASME. J. Vib. Acoust. February 2017; 139(1): 011002. https://doi.org/10.1115/1.4034626
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