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Design of Intelligent Control Using Dynamic Petri, CMAC, and BCMO for Nonlinear Systems with Uncertainties

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  • Van-Truong Nguyen

    (Faculty of Mechatronics, SMAE, Hanoi University of Industry, Hanoi 10000, Vietnam)

  • Duc-Hung Pham

    (Faculty of Electrical and Electronic Engineering, Hung Yen University of Technology and Education, Hung Yen 17000, Vietnam)

  • V. T. Mai

    (Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam)

  • Hoang-Nam Nguyen

    (Ecole Nationale de l’Aviation Civile, 31400 Toulouse, France)

  • Minh-Tri Phan

    (Department of Applied Engineering Technology, University of Science and Technology of Hanoi, Hanoi 11307, Vietnam)

Abstract

This paper presents a novel dynamic Petri fuzzy neural network (DPFNN) for controlling the position of a metal ball in a magnetic levitation system (MLS). The DPFNN reduces parameter learning costs by combining Petri nets and fuzzy frameworks. Given the nonlinear and uncertain dynamics of the MLS, an adaptive DPFNN control system was developed for high-precision position control. The parameter set has been optimized using the BCMO algorithm for the best performance. The desired system stability and control performance can be achieved by the proposed control system.

Suggested Citation

  • Van-Truong Nguyen & Duc-Hung Pham & V. T. Mai & Hoang-Nam Nguyen & Minh-Tri Phan, 2025. "Design of Intelligent Control Using Dynamic Petri, CMAC, and BCMO for Nonlinear Systems with Uncertainties," Mathematics, MDPI, vol. 13(17), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:17:p:2825-:d:1740291
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