Optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system

In pursue of comfortable driving in unpaved and off-roads, intelligent methods are used to improve the suspension systems in the vehicles. Semi-active suspension systems outperformed passive and active suspension systems because it contains an intelligent actuator that can give the appropriate force to dissipate unwanted vibration using intelligent and real-time controllers. This study focuses on the Magneto- rheological (MR) fluid damper, integrated with a Fuzzy PID controller, which is a widely investigated actuator within the domain of intelligent semi-active suspension systems. It is noteworthy, however, that the Fuzzy logic algorithm employed in the Fuzzy PID controller does not fully qualify as a true real-time control system. This is because the fuzzy rules are predefined offline, based on existing knowledge, and may not adequately address sudden, unpredictable vibrations encountered in real-time driving conditions. Traditionally, the refinement of fuzzy rules relies on offline optimization techniques, including the Genetic Algorithm (GA), Artificial Bee Colony (ABC) method, and the Firefly Algorithm (FA). This study introduces the Grey Wolf Optimizer (GWO) algorithm to fine-tune the output gains of the fuzzy logic component, aiming to boost the efficiency of the PID segment within the Fuzzy PID controller online. The GWO algorithm stands out for its ease of application, rapid convergence, and optimal convergence results. The GWO is applied to enhance the Fuzzy logic output gains to increase the performance of PID portion of the Fuzzy-PID controller. The developed Fuzzy GWO PID controller was tested in two manners, first by simulation using MATLAB Simulink with sinusoidal and random disturbances. Then the model was tested experimentally in a quarter car test rig using different disturbances by means of a pneumatic actuator as an excitation. The test was developed at the control lab, Faculty of Engineering in UPM. Simulation test outcomes revealed that for the semi-active suspension system governed by the Fuzzy GWO PID, the Fuzzy PID controllers, and the passive suspension system, the Root Mean Square (RMS) value of body displacement were reduced by 5.33% and 13.28%. The Fuzzy GWO PID controller demonstrated superior performance across all metrics. In the experimental tests on sinusoidal disturbance, Fuzzy GWO PID controller improved the vehicle’s ride comfort by 16.13%, 21.57%, 30.77% better than the Fuzzy PID controller, and 39.53%, 6.06%, 16.67% better than the passive system. While in the random disturbance, the Fuzzy GWO PID controller improved the vehicle’s ride comfort by 46.55%, 45.61% better than the Fuzzy PID controller and passive system, respectively. The RMS values of the body acceleration of the Fuzzy GWO PID controller in the sinusoidal disturbance and random disturbance are 2.6e −3 , 6.2e −3 , 4.5e −3 and 3.1e −3 respectively. A most remarkable point on these results is that the RMS value fall on the “Not uncomfortable” level according to ISO 2631-1:1997 standards since the RMS value recorded in these tests are less than 0.315. This superiority is attributed to its validation across diverse road conditions. By using this developed controller in any other real-time application, it will improve the performance to the highest levels without the need for a previous knowledge base for designing a real-time Fuzzy-PID controller.

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