Genetic Algorithm On Line Controller for the Flexible Inverted Pendulum Problem
Elmer P. Dadios*, Patrick S. Fernandez**, and David J. Williams***
*Department of Manufacturing Engineering and Management, 2401 Taft Avenue, De La Salle University, Manila 1004, Philippines
**National Power Corporation, Diliman, Quezon City 1004, Philippines
***Loughborough University, Loughborough Leicestershire, LE11 3TU, U.K.
This paper presents a real time controller for a highly non-linear system. The Flexible Pole-Cart Balancing Problem (FPCBP) is used as the test case to investigate the learning capability of Genetic Algorithm (GA) in physical application. The controller developed is initially trained using a set of data taken from on line dynamics of the flexible pole cart balancing system. Based from the physical data of the system, the weights W1 to W6 are optimized by the genetic algorithm in order to determine the correct value of the force applied to the cart. The trained GA-based controller then controls the physical Flexible Pole-Cart Balancing system for infinite time. Analysis on the behavior of the GA model developed is presented. Results of the physical experiments show that the controller developed is accurate, adaptive and robust.
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