Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
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...
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| Format: | Monograph |
| Language: | English |
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Universiti Sains Malaysia
2018
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| Online Access: | http://eprints.usm.my/53592/1/Optimization%20Of%20Pid%20Controller%20Using%20Grey%20Wolf%20Optimzer%20And%20Dragonfly%20Algorithm_Nik%20Muhammad%20Aiman%20Nik%20Mohamed%20Hazli_E3_2018.pdf |
| _version_ | 1797013053970055168 |
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| author | Nik Mohamed Hazli, Nik Muhammad Aiman |
| author_facet | Nik Mohamed Hazli, Nik Muhammad Aiman |
| author_sort | Nik Mohamed Hazli, Nik Muhammad Aiman |
| collection | USM |
| description | 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. |
| first_indexed | 2024-03-06T15:56:35Z |
| format | Monograph |
| id | usm.eprints-53592 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:56:35Z |
| publishDate | 2018 |
| publisher | Universiti Sains Malaysia |
| record_format | dspace |
| spelling | usm.eprints-535922022-07-26T01:26:51Z http://eprints.usm.my/53592/ Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm Nik Mohamed Hazli, Nik Muhammad Aiman T Technology TK Electrical Engineering. Electronics. Nuclear Engineering 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. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53592/1/Optimization%20Of%20Pid%20Controller%20Using%20Grey%20Wolf%20Optimzer%20And%20Dragonfly%20Algorithm_Nik%20Muhammad%20Aiman%20Nik%20Mohamed%20Hazli_E3_2018.pdf 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) |
| spellingShingle | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Nik Mohamed Hazli, Nik Muhammad Aiman Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title | Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title_full | Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title_fullStr | Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title_full_unstemmed | Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title_short | Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm |
| title_sort | optimization of pid controller using grey wolf optimzer and dragonfly algorithm |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | http://eprints.usm.my/53592/1/Optimization%20Of%20Pid%20Controller%20Using%20Grey%20Wolf%20Optimzer%20And%20Dragonfly%20Algorithm_Nik%20Muhammad%20Aiman%20Nik%20Mohamed%20Hazli_E3_2018.pdf |
| work_keys_str_mv | AT nikmohamedhazlinikmuhammadaiman optimizationofpidcontrollerusinggreywolfoptimzeranddragonflyalgorithm |