Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle
The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estima...
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MDPI AG
2022-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/6/1001 |
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| author | Faisal Altaf Ching-Lung Chang Naveed Ishtiaq Chaudhary Muhammad Asif Zahoor Raja Khalid Mehmood Cheema Chi-Min Shu Ahmad H. Milyani |
| author_facet | Faisal Altaf Ching-Lung Chang Naveed Ishtiaq Chaudhary Muhammad Asif Zahoor Raja Khalid Mehmood Cheema Chi-Min Shu Ahmad H. Milyani |
| author_sort | Faisal Altaf |
| collection | DOAJ |
| description | The knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials. |
| first_indexed | 2024-03-09T13:25:29Z |
| format | Article |
| id | doaj.art-962bc8762f0c4567a6c7221f1a2a1de6 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:25:29Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-962bc8762f0c4567a6c7221f1a2a1de62023-11-30T21:25:17ZengMDPI AGMathematics2227-73902022-03-01106100110.3390/math10061001Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation PrincipleFaisal Altaf0Ching-Lung Chang1Naveed Ishtiaq Chaudhary2Muhammad Asif Zahoor Raja3Khalid Mehmood Cheema4Chi-Min Shu5Ahmad H. Milyani6Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanDepartment of Electrical Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, PakistanDepartment of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, TaiwanDepartment of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe knacks of evolutionary and swarm computing paradigms have been exploited to solve complex engineering and applied science problems, including parameter estimation for nonlinear systems. The population-based computational heuristics applied for parameter identification of nonlinear systems estimate the redundant parameters due to an overparameterization problem. The aim of this study was to exploit the key term separation (KTS) principle-based identification model with adaptive evolutionary computing to overcome the overparameterization issue. The parameter estimation of Hammerstein control autoregressive (HC-AR) systems was conducted through integration of the KTS idea with the global optimization efficacy of genetic algorithms (GAs). The proposed approach effectively estimated the actual parameters of the HC-AR system for noiseless as well as noisy scenarios. The simulation results verified the accuracy, convergence, and robustness of the proposed scheme. While consistent accuracy and reliability of the designed approach was validated through statistical assessments on multiple independent trials.https://www.mdpi.com/2227-7390/10/6/1001Hammerstein nonlinear systemsparameter estimationbioinspired computinggenetic algorithms |
| spellingShingle | Faisal Altaf Ching-Lung Chang Naveed Ishtiaq Chaudhary Muhammad Asif Zahoor Raja Khalid Mehmood Cheema Chi-Min Shu Ahmad H. Milyani Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle Mathematics Hammerstein nonlinear systems parameter estimation bioinspired computing genetic algorithms |
| title | Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle |
| title_full | Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle |
| title_fullStr | Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle |
| title_full_unstemmed | Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle |
| title_short | Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle |
| title_sort | adaptive evolutionary computation for nonlinear hammerstein control autoregressive systems with key term separation principle |
| topic | Hammerstein nonlinear systems parameter estimation bioinspired computing genetic algorithms |
| url | https://www.mdpi.com/2227-7390/10/6/1001 |
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