Horned lizard defense tactics optimization for robust FOPID control in vehicle active suspension systems

This research introduces the Horned Lizard Defense Tactics Algorithm (HLOA), a novel bio-inspired optimization method, for the optimal tuning of Fractional-Order Proportional-Integral-Derivative (FOPID) controllers within vehicle active suspension systems. The HLOA is designed to concurrently optimize all five FOPID parameters (Kp, Ki, Kd, λ, μ) through a weighted cost function. This function aims to balance the enhancement of ride comfort, primarily by minimizing sprung mass acceleration, with the assurance of handling stability through the limitation of suspension deflection. Extensive simulation studies, employing a double-bump road excitation model, demonstrate the superior performance of the HLOA-optimized FOPID controller. Comparative analysis indicates that this optimized system outperforms passive suspension systems, classical PID controllers,

and non-optimized FOPID configurations. Specifically, the HLOA-FOPID controller achieves significant reductions in Root Mean Square (RMS) sprung mass acceleration by 26.6% and RMS suspension deflection by 72.6% relative to a passive benchmark. Furthermore, it reduces RMS sprung mass displacement by 16.2% and relative suspension speed by 73.5%. Beyond nominal performance, the controller exhibits notable robustness against practical parameter uncertainties, maintaining ride comfort within ±0.73% for mass variations up to ±25% and showing a minimal standard deviation in RMS acceleration across multiple runs.

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