PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization
In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4767 |
| _version_ | 1797606592590381056 |
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| author | Huaiqin Liu Qinghe Yu Qu Wu |
| author_facet | Huaiqin Liu Qinghe Yu Qu Wu |
| author_sort | Huaiqin Liu |
| collection | DOAJ |
| description | In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller. |
| first_indexed | 2024-03-11T05:17:22Z |
| format | Article |
| id | doaj.art-e617576214d843bd89c4ad0a242af3fe |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:17:22Z |
| publishDate | 2023-04-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e617576214d843bd89c4ad0a242af3fe2023-11-17T18:08:56ZengMDPI AGApplied Sciences2076-34172023-04-01138476710.3390/app13084767PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf OptimizationHuaiqin Liu0Qinghe Yu1Qu Wu2School of Information and Control Engineering, Qingdao University of Technology, No. 777 Jialingjiang East Rd., Qingdao 266525, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, No. 777 Jialingjiang East Rd., Qingdao 266525, ChinaSchool of Information and Control Engineering, Qingdao University of Technology, No. 777 Jialingjiang East Rd., Qingdao 266525, ChinaIn processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller.https://www.mdpi.com/2076-3417/13/8/4767gray wolf optimization algorithmback propagation neural networkactivation functionsPID controller |
| spellingShingle | Huaiqin Liu Qinghe Yu Qu Wu PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization Applied Sciences gray wolf optimization algorithm back propagation neural network activation functions PID controller |
| title | PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization |
| title_full | PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization |
| title_fullStr | PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization |
| title_full_unstemmed | PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization |
| title_short | PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization |
| title_sort | pid control model based on back propagation neural network optimized by adversarial learning based grey wolf optimization |
| topic | gray wolf optimization algorithm back propagation neural network activation functions PID controller |
| url | https://www.mdpi.com/2076-3417/13/8/4767 |
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