This study presents a novel approach to analyze and fine-tune control system algorithms for semi-active suspension systems. In the case off road vehicles (example military vehicles), the ability of the suspension control system to keep the vehicle ride quality in an acceptable range is of paramount importance. Though limited in their intervention, semi-active suspensions are less expensive to design and consume far less energy in comparison to active suspension systems. In recent times, research in semi-active suspension systems has continued to advance with respect to their capabilities, narrowing the gap between semi-active and fully active suspension systems. This study investigates the usage of a semi-active suspension with a skyhook-groundhook hybrid controller. As a first step, sensitivity analysis of the controller performance to varying vehicle/road input parameters is conducted. This approach utilizes sensitivity analysis and one-factor-at-a-time method (OFAT) to find and reach the optimum point of vehicle suspension settings. Furthermore, real-time tuning of the mentioned controller is studied. Real-time tuning will help keep the ride quality of the vehicle close to its optimum point even during situations when the vehicle/road input parameters change. A quarter-car model is used for all the simulations and sensitivity analysis.
- Dynamic Systems and Control Division
Analysis and Enhancement of Hybrid Skyhook-Groundhook Semi-Active Suspension Controller for Optimal Performance
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Ghasemalizadeh, O, Taheri, S, Singh, A, & Singh, KB. "Analysis and Enhancement of Hybrid Skyhook-Groundhook Semi-Active Suspension Controller for Optimal Performance." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T45A012. ASME. https://doi.org/10.1115/DSCC2017-5381
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