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    Area of Science:

    • * Mechanical Engineering
    • * Control Systems
    • * Computational Intelligence

    Background:

    • * Four-bar mechanisms are vital in industrial processes but challenging to control due to inherent nonlinearities and external disturbances.
    • * Precise speed regulation is essential for operational efficiency and product quality in applications utilizing these mechanisms.

    Purpose of the Study:

    • * To investigate and compare various Pareto-front approximation search strategies for adaptive controller tuning.
    • * To evaluate these strategies within the context of speed regulation for four-bar mechanisms using online multiobjective metaheuristic optimization.

    Main Methods:

    • * Implementation and analysis of dominance-based, decomposition-based, metric-driven, and hybrid Pareto-front approximation approaches.
    • * Specific algorithms examined include NSGA-II, MOEA/D-DE, SMSEMOA, NSGA-III, and a novel metric-driven algorithm (HV-MODE).
    • * Application of these methods to the speed regulation problem of four-bar mechanisms.

    Main Results:

    • * Demonstrated effectiveness of adaptive controller tuning through online multiobjective metaheuristic optimization.
    • * Comparative analysis using descriptive and nonparametric statistical methods confirmed the efficacy of the tested approaches.
    • * Metric-driven search approaches, particularly the proposed HV-MODE, showed significant advantages.

    Conclusions:

    • * Online multiobjective metaheuristic optimization is a powerful technique for adaptive controller tuning in complex mechanical systems.
    • * Metric-driven search strategies offer superior performance in approximating Pareto fronts for four-bar mechanism speed regulation.
    • * The HV-MODE algorithm presents a promising advancement for enhancing control system robustness and efficiency.