The multi-objective optimal control design usually generates hundreds or thousands of Pareto optimal solutions. How to assist an user to select an appropriate controller to implement is a post-processing 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 then develop a switching strategy to select controls from different clusters to improve the performance. Numerical results show that the switching control algorithm is quite promising.
Volume Subject Area:
Advances in Nonlinear and Optimal Control
This content is only available via PDF.
Copyright © 2016 by ASME
You do not currently have access to this content.
