DocumentCode :
2974083
Title :
Non-linear constrained optimal control problem: A hybrid PSO-GA-Based discrete augmented lagrangian approach
Author :
Khoukhi, Amar ; Al-Sunni, Fouad M. ; Khalid, Haris M. ; Rizvi, S.Z.
Author_Institution :
Syst. Eng. Dept., K.F.U.P.M., Dhahran, Saudi Arabia
fYear :
2011
fDate :
18-20 March 2011
Firstpage :
1
Lastpage :
6
Abstract :
This work deals with the optimal control problem which has been proposed to solve using the discrete augmented lagrangian based non-linear programming approach. It is shown that this technique guarantee a satisfactory performance in the face of both optimality by minimizing the energy and maximizing the output. Later on, the optimization has been more effective by using PSO-GA-Based Optimization to achieve the optimal value of Lagrange Multipliers and required dynamic parameters and optimally controlling the dynamics. The designed scheme has been successfully tested through extensive simulation. The successful use of the proposed scheme encourages their extension to other physical systems. The proposed scheme is evaluated extensively on a two-tank process used in industry exemplified by a benchmarked laboratory scale coupled-tank system.
Keywords :
genetic algorithms; nonlinear control systems; nonlinear dynamical systems; nonlinear programming; optimal control; particle swarm optimisation; Lagrange multipliers; benchmarked laboratory scale coupled-tank system; genetic algorithm; hybrid PSO-GA-based discrete augmented Lagrangian approach; nonlinear constrained optimal control problem; nonlinear programming; particle swarm optimization; two-tank process; Equations; Genetic algorithms; Iterative methods; Kalman filters; Optimal control; Optimization; Process control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Information Processing Society (NAFIPS), 2011 Annual Meeting of the North American
Conference_Location :
El Paso, TX
ISSN :
Pending
Print_ISBN :
978-1-61284-968-3
Electronic_ISBN :
Pending
Type :
conf
DOI :
10.1109/NAFIPS.2011.5751926
Filename :
5751926
Link To Document :
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