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Intelligent PID Controller with Cuckoo Search Algorithm and Particle Swarm Optimisation for Semi-Active Suspension System Using Magneto-Rheological Damper


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DOI: https://doi.org/10.15866/ireme.v16i3.20216

Abstract


This article presents the employment of Cuckoo Search (CS) Algorithm to tune a Proportional Integral Derivative (PID) controller for a semi-active suspension system in order to improve and enhance ride comfort and stability in a vehicle. In the meantime, the Cuckoo Search Algorithm has been compared between existing intelligent approaches such as Particle Swarm Optimisation (PSO) and passive system. The performances of the Proportional Integral Derivative controller have been optimised via Cuckoo Search Algorithm and Particle Swarm Optimisation, respectively. The mean square error for the suspension system has been determined as an objective function for the proposed controller's optimisation. The performance of the proposed PID-CS controller has been compared to existing PID-PSO controller and passive system in terms of body acceleration and body displacement. The bump and hole together with random road profile have been set as the source of disturbance for the system. The simulated results have demonstrated that the PID-CS controller has been able to improve significantly ride comfort between 14-16% in terms of mean square error for both body displacement and body acceleration.
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Keywords


Semi-Active (SA) Suspension System; Quarter Car Model; Magneto-Rheological (MR) Damper; Particle Swarm Optimisation; Cuckoo Search (CS) Algorithm; PID Controller

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References


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