Practical solution for attenuating industrial heavy vehicle vibration: A new gain-adaptive coordinated suspension control system

Abstract

Prioritizing the improvement of truck driver's ride comfort is crucial for the health and well-being of drivers, driver retention, safety, overall productivity, regulatory compliance, and customer satisfaction. As a solution, adaptive suspension systems are developed to optimize suspension performances. In this study, a novel integrated Skyhook-LQR algorithm is introduced, which aims to simultaneously improve the sprung mass dynamics in vertical, pitch, and roll directions. Most importantly, it requires affordable computational cost and can be processed on an automotive-grade microcontroller. Besides, it is impossible to find one set of optimum control gains for rapid-changing disturbances since the vehicle may be driven on various road surfaces. A gain-adaptive algorithm is developed to intelligently adjust the LQR's output penalty matrix Q according to onboard sensor measurements to fill this gap. The performance and effectiveness of the proposed techniques are experimentally examined based on a scaled-down cab-over-engine model and a Stewart Platform. The vehicle responses and disturbance inputs are measured by two 6-axis IMUs and four height sensors, and all the messages are transmitted through the CAN Bus. The unmeasurable states are estimated by a Kalman filter observer. The experimental results validated that the integrated Skyhook-LQR has excellent potential in suspension coordinated control, which significantly optimizes ride quality. Meanwhile, the gain-adaptive algorithm detected vehicle motions and provided efficient gain scheduling decisions, by which the undesired vibrations and shocks were further attenuated to some extent.