Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm

Abstract

In the design of predictive controllers (MPC), parameterisation of degrees of freedom by Laguerre functions, has shown to improve the controller performance and feasible region. However, an open question remains: how to select the optimal tuning parameters? Moreover, optimality will depend on the size of the feasible region of the controller, the system's closed-loop performance and the online computational cost of the algorithm. This paper develops a method for a systematic selection of tuning parameters for a parameterised predictive control algorithm. In order to do this, a multiobjective problem is posed and then solved using a multiobjective evolutionary algorithm (MOEA) given that the objectives are in conflict. Numerical simulations show that the MOEA is a useful tool to obtain a suitable balance between feasibility, performance and computational cost.

  1. R C Gutiérrez-Urquídez, G Valencia-Palomo, O M Rodríguez-Elias and L Trujillo. Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm. Applied Soft Computing, pages 326 - 338, 2015. URL, DOI BibTeX

    @article{,
    	year = 2015,
    	issn = "1568-4946",
    	journal = "Applied Soft Computing",
    	doi = "http://dx.doi.org/10.1016/j.asoc.2015.02.033",
    	title = "Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm",
    	url = "http://www.sciencedirect.com/science/article/pii/S1568494615001349",
    	publisher = "Springer Netherlands",
    	author = "R.C. Guti\'{e}rrez-Urqu\'{i}dez and G. Valencia-Palomo and O.M. Rodr\'{i}guez-Elias and L. Trujillo",
    	pages = "326 - 338"
    }
    

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