Nur Iffah Mohamed Azmi creator text my Kuantan, Pahang UMP 2015 monographic eng

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xviii, 121 p. : ill. ; 30 cm. + 1 CD-ROM Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Especially on hydraulic positioning system that is highly nonlinear and difficult to be controlled whereby PID parameters needs to be tuned to obtain optimum performance criteria. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods.Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey-Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the limit of change in particle velocity and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposedPSO-PID with the Variable Weight Grey-Taguchi DOE and the classical ZieglerNichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight GreyTaguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PIDparameter by applying the PSO-PID algorithm together with the Variable Weight GreyTaguchi DOE method as a good tuning method in the hydraulic positioning system. specialized Nur Iffah Mohamed Azmi Faculty of Manufacturing Engineering Thesis (Master of Engineering (Manufacturing)) -- Universiti Malaysia Pahang – 2015 Bibliography : p. 107-113 Faculty of Manufacturing Engineering Dissertations Universities and Colleges Dissertations Theses THE0005243(Local) UMP 160316 20251117113321.0 vtls000093889