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Journal Article

New Attempts on Vehicle Suspension Systems Modeling and Its Application on Dynamical Load Analysis

2011-09-13
2011-01-2171
Suspension system dynamics can be obtained by various methods and vehicle design has gained great advantages over the dynamics analysis. By employing the new Udwadia-Kalaba equation, we endeavor some attempts on its application to dynamic modeling of vehicle suspension systems. The modeling approach first segments the suspension system into several component subsystems with kinematic constraints at the segment points released. The equations of motion of the unconstrained subsystems are thus easily obtained. Then by applying the second order constraints, the suspension system dynamics is then obtained. The equations are of closed-form. Having the equations obtained, we then show its application on dynamical load analysis. The solutions for the dynamical loads at interested hard points are obtained. We use the double wishbone suspension to show the systematic approach is easy handling.
Technical Paper

Robust Braking/Driving Force Distribution and Active Front Steering Control of Vehicle System with Uncertainty

2011-09-13
2011-01-2145
Uncertainties present a large concern in actual vehicle motion and have a large effect on vehicle system control. We attempt a new robust control design approach for braking/driving force distribution and active front steering of vehicle system with uncertain parameters. The braking/driving force distribution control is equivalently studied as the integral direct yaw moment control. Then the control design is carried out by using a state-space vehicle model with embedded fuzzy uncertainties. By taking the compensated front wheel steering angle and the direct yaw moment as the control inputs, a feedback control that aims to compensate the system uncertainty is proposed. In a quite different angle, we employ fuzzy descriptions of the uncertain parameters. The controlled system performance is deterministic, and the control is not if-then rules-based. Fuzzy descriptions of the uncertain parameters are used to find an optimal control gain.
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