Title :
LQ evolution algorithm optimizer for model predictive control at model uncertainty
Author_Institution :
Dept. of Chem. Eng., King Khalid Univ., Khalid, Saudi Arabia
Abstract :
This paper presents an evolution algorithm as a powerful optimisation technique for tuning Model Based Predictive Control (MBPC) at the implications of different levels of model uncertainties. Although Standard Genetic Algorithms (SGAs) are proven to successfully tune and optimise MBPC parameters when no model mismatch. SGAs are trapped in a local optimum at the price of model uncertainty. The multi-objective evaluation algorithms are capable to incorporate many objective functions that can meet simultaneously robust control design objective functions. These promising techniques are successfully implemented to stabilised MBPC at high model uncertainty.
Keywords :
genetic algorithms; modelling; predictive control; robust control; LQ evolution algorithm optimizer; MBPC parameters; SGA; high model uncertainty; model based predictive control; multiobjective evaluation algorithms; optimisation technique; robust control; stabilised MBPC; standard genetic algorithms; Abstracts; Genetic algorithms; Genetics; Optical fibers; Standards; Tuning; Control Tuning; Model Base Predictive Control; Multi-objective Evolution Algorithm;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2014 14th International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-8-9932-1506-9
DOI :
10.1109/ICCAS.2014.6987752