| Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks |
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| 引用本文： | 张燕,陈增强,袁著祉. Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks[J]. 系统工程与电子技术(英文版), 2003, 14(1) |
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| 作者姓名： | 张燕 陈增强 袁著祉 |
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| 作者单位： | Zhang Van，Chen Zengqiang2 & Yuan ZhuzhiDept. of Automation，Hebei University of Technoloy，Tianjin 300130，P. R. China; Dept. of Automation，Nankai University，Tianjin 300071，P. R. China |
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| 基金项目： | This project was supported by the National Natural Science Foundation of China(60174021)，Natural Science Foundation Key Project of Tianjin(013800711). |
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| 摘 要： | Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.
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Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks |
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| Zhang Van,Chen Zengqiang , Yuan ZhuzhiDept. of Automation,Hebei University of Technoloy,Tianjin ,P. R. China, Dept. of Automation,Nankai University,Tianjin ,P. R. China. Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks[J]. Journal of Systems Engineering and Electronics, 2003, 14(1) |
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| Authors: | Zhang Van Chen Zengqiang & Yuan ZhuzhiDept. of Automation Hebei University of Technoloy Tianjin P. R. China Dept. of Automation Nankai University Tianjin P. R. China |
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| Affiliation: | 1. Dept. of Automation, Hebei University of Technoloy, Tianjin 300130, P. R. China; Dept. of Automation, Nankai University, Tianjin 300071, P. R. China
2. Dept. of Automation, Nankai University, Tianjin 300071, P. R. China |
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| Abstract: | After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks is introduced, an intelligent FID controller is adopted to correct the errors including identified model errors and accumulated errors produced in the recursive process. Characterized by predictive control, this method can achieve a good control accuracy and has good robustness. A simulation study shows that this control algorithm is very effective. |
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| Keywords: | Multi-step-ahead predictive control Recurrent neural networks Intelligent PID control. |
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