Abstract
In this study, a Granular Function Fractional-Order Proportional-Integral-Derivative (GFFOPID) controller is developed for nonlinear suspension systems. Initially, a Fractional Order Proportional-Integral-Derivative (FOPID) controller is employed, and its parameters are tuned using fuzzy systems to enhance performance under disturbances and uncertainties. Subsequently, Particle Swarm Optimization is applied to further optimize these parameters for improved control efficacy. The Fractional Order fuzzy Proportional-Integral-Derivative controller, which involves fuzzy sets and inference process, can suffer from extended computation times. This issue is mitigated through the introduction of granular computing, which simplifies the fuzzy control process by substituting traditional inference with granular sampling functions. Experimental results show that the GFFOPID controller effectively improves both the maneuvering stability and ride comfort of the vehicle. Furthermore, the use of granular functions effectively resolves the rule base explosion issue, reduces computational complexity, and significantly enhances the controller’s efficiency.
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