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Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle

Author

Listed:
  • Faisal Altaf

    (Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ching-Lung Chang

    (Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Naveed Ishtiaq Chaudhary

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Muhammad Asif Zahoor Raja

    (Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Khalid Mehmood Cheema

    (Department of Electrical Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan)

  • Chi-Min Shu

    (Department of Safety, Health, and Environmental Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan)

  • Ahmad H. Milyani

    (Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Faisal Altaf & Ching-Lung Chang & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Chi-Min Shu & Ahmad H. Milyani, 2022. "Adaptive Evolutionary Computation for Nonlinear Hammerstein Control Autoregressive Systems with Key Term Separation Principle," Mathematics, MDPI, vol. 10(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:1001-:d:775746
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    References listed on IDEAS

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    1. Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja & Zeshan Aslam Khan & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Hierarchical Quasi-Fractional Gradient Descent Method for Parameter Estimation of Nonlinear ARX Systems Using Key Term Separation Principle," Mathematics, MDPI, vol. 9(24), pages 1-14, December.
    2. YoungSu Yun & Anudari Chuluunsukh & Mitsuo Gen, 2020. "Sustainable Closed-Loop Supply Chain Design Problem: A Hybrid Genetic Algorithm Approach," Mathematics, MDPI, vol. 8(1), pages 1-19, January.
    3. Cao, Wenping & Zhu, Quanxin, 2022. "Stability of stochastic nonlinear delay systems with delayed impulses," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    4. Tissaoui, Kais, 2019. "Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 232-249.
    5. Hasnat Bin Tariq & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2021. "Maximum-Likelihood-Based Adaptive and Intelligent Computing for Nonlinear System Identification," Mathematics, MDPI, vol. 9(24), pages 1-23, December.
    6. Martin Ćalasan & Mihailo Micev & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2020. "Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
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    Cited by:

    1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    2. Mehmood, Khizer & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Cheema, Khalid Mehmood & Raja, Muhammad Asif Zahoor & Shu, Chi-Min, 2023. "Novel knacks of chaotic maps with Archimedes optimization paradigm for nonlinear ARX model identification with key term separation," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
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    4. Naveed Ahmed Malik & Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja, 2023. "Firefly Optimization Heuristics for Sustainable Estimation in Power System Harmonics," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    5. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Khalid Mehmood Cheema & Ahmad H. Milyani, 2022. "Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    6. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Khalid Mehmood Cheema & Zeshan Aslam Khan & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdulellah Alsulami, 2023. "Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

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