Simulation-Based Investigation of Adaptive Suspension Control for Regional Road Conditions in Tigray

Abstract

This thesis presents the design, modeling, simulation, and performance evaluation of an adaptive suspension control system developed to improve vehicle dynamics under the diverse road conditions of the Tigray region, Ethiopia. Suspension systems are fundamental in enhancing ride comfort, handling, and overall vehicle stability. Conventional passive suspensions, while simple and cost-effective, lack adaptability to the rapidly changing and uneven road conditions prevalent in developing regions. In response, researchers have introduced various intelligent control techniques—such as PID, Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS)—to address these challenges. However, existing studies still face limitations in real-time adaptability, nonlinear response management, and system robustness across unpredictable terrains. To overcome these challenges, this study proposes a hybrid PID–ANFIS adaptive suspension control approach, combining the fast response of the PID controller with the learning and adaptability of ANFIS. A quarter-car model of a light-duty vehicle was developed in MATLAB/Simulink to simulate various representative road conditions, including paved, unpaved, bump, and hilly terrains. The controller’s performance was evaluated using key dynamic metrics: ride comfort (weighted RMS acceleration), suspension travel, and road holding ability. Simulation results demonstrated that the hybrid PID–ANFIS controller outperformed both the classical PID and passive suspension systems. Specifically, body acceleration was reduced by over 80%, suspension travel was maintained within safe mechanical limits, and tire force variation was minimized, improving road holding stability. The overshoot decreased from 72.33% (PID) to 19.73% (PID–ANFIS), while rise time improved from 34.71 ms to 12.59 ms, and the RMS error reduced from 0.05784 (passive) to 0.00026 (PID–ANFIS). Compared to prior studies reporting 70–78% improvement using hybrid controllers, the proposed system achieved higher performance gains due to optimized parameter tuning and adaptive learning capabilities. The results confirm that the proposed hybrid PID–ANFIS controller is an effective, terrain-adaptive solution capable of improving ride comfort, stability, and safety for vehicles operating in challenging regional road conditions. This work contributes a region-specific adaptive suspension model that can be applied to improve vehicle performance in developing areas with similar infrastructure characteristics.