Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm

Nik Mohamed Hazli, Nik Muhammad Aiman (2018) Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)

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Optimisation is a method to find a balance performance when the design has to compromise between a certain factors, which affects fitness and cost. In engineering field, one of the common optimisation problem is optimisation of PID controller. Optimisation is difficult to optimise as there are three parameters that need to be tuned, Kp, Integral parameter, Ki, and derivative parameter, Kd. In this research, swarming intelligence is used to solve optimisation problem. Grey Wolf Optimizer and Dragonfly Algorithm were chosen. Three plant system were used in this study. First system is based on the ball and hoop system and second system is based on the DC servo motor. Last system is based on the brushed DC motor. Objective function in this research, cost function was chosen. The criteria of the cost function are low peak overshoot, Mp, low steady-state error, ess, low settling time, Ts, and low rise time, Tr. However, to fully utilize the algorithm, the parameter of the algorithm need to be set properly. In this case, the right number of the search agents for both algorithm. The stopping criteria also need to be identified. In this study, maximum number of iterations is the stopping criteria. The expected result is the algorithms are able to optimise the PID controller. However, the performance of system is expected to be different from different algorithm.

Item Type: Monograph (Project Report)
Subjects: T Technology
T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Monograph
Depositing User: Mr Engku Shahidil Engku Ab Rahman
Date Deposited: 26 Jul 2022 01:26
Last Modified: 26 Jul 2022 01:26
URI: http://eprints.usm.my/id/eprint/53592

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