A novel design of high-sensitive fuzzy PID controller |
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| Affiliation: | 1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia;2. Foundation of Technical Education Baghdad, Iraq Ministry of Electricity;3. Faculty of computing, Universiti Teknologi Malaysia, 81310 Johor, Malaysia;1. Universidad Autónoma de Querétaro, Facultad de Ingeniería. A.P. 3-24, C.P. 76150, Querétaro, Qro., Mexico;2. CIDESI, Dirección de Investigación y Posgrado. Av. Playa Pie de la Cuesta No. 702, Desarrollo San Pablo, C.P. 76130, Querétaro, Qro., Mexico;1. Departamento de Lenguajes y Ciencias de la Computación, University of Malaga, Malaga, Spain;2. VSB-Technical University of Ostrava, Czech Republic;1. School of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, China;2. College of Computer Science and Technology, Jilin University, Changchun 130012, China;3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;4. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;5. College of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China;1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, PR China;2. School of Sciences, Dalian Ocean University, Dalian 116023, PR China;1. Department of Ship and Marine Technology, Izmir Katip Celebi University, Cigli 35620, Izmir, Turkey;2. Department of Business Administration, Yildiz Technical University, Besiktas 34349, Istanbul, Turkey;3. International Maritime Research Center (IMaRC), Kobe University, Higashinada 658-0022, Kobe, Japan |
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| Abstract: | 
A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integral-derivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. The rule base is developed by proposing a feature extraction for genetic neural fuzzy PID controller through integrating the GA with radial basis function neural network. The GNFPID controller is found to possess excellent features of easy implementation, stable convergence characteristic, good computational efficiency and high-quality solution. Our simulation provides high sensitive response (∼0.005 s) of an AVR system compared to the real-code genetic algorithm (RGA), a linear-quadratic regulator (LQR) method and GA. We assert that GNFPID is highly efficient and robust in improving the sensitive response of an AVR system. |
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| Keywords: | AVR system GA PID controller RBF-NN Rule base Sugeno fuzzy logic |
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