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Good handling stability becomes very important for heavily-laden electric wheel dump trucks that are operated on rough roads. To improve handling stability of mining dump trucks, nonlinear stiffness and nonlinear damping of the hydro-pneumatic suspension were considered as optimization variables. In this paper, based on the Daubechies wavelet's compactness and regularization and least-square method, the nonlinear stiffness and damping are identified. In order to verify the results of the parameter identification, the multi-body system dynamic model of the truck was built in ADAMS/view. By comparing the simulated results and tested ones, we find acceleration-history and power spectral density of acceleration are very close. And then, based on the approximate model method, the optimization model was built in ISIGHT. The nitrogen column and the orifice diameter were defined as the design variables. Finally, the handling stability was optimized by applying the genetic algorithms method.

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.