This paper considers a two degree of freedom quarter-car model of a four wheel vehicle with nonlinear spring travelling at a constant speed on a random road surface with a preview control to obtain the optimal vehicle response. The nonlinear spring behavior is assumed as a hysteresis and modelled by the Bouc–Wen model. This nonlinear vehicle model is linearized by using statistical linearization approach. The root mean square values of the control force, suspension stroke and road holding characteristics are determined by spectral decomposition method. The results obtained for the equivalent linearized model using the SDM are validated with Monte Carlo simulation, and the results show that the vehicle performs better using preview control.

This article aims to determine the optimum parameters of a half-car model passive suspension vehicle passing on a random road. The optimum parameters are obtained based on the response of linear quadratic regulator control with a look-ahead preview for attaining the passive suspension performance nearly equivalent to the active suspension performance. The optimum parameters are estimated by equalizing mean square suspension controlling forces of passive and active vehicle models and subsequently minimizing the performance error between the two systems. The response of passive suspension with optimized parameters matches approximately with the active suspension response, with respect to ride comfort and road holding.

A two-axle rail vehicle having solid axle wheel-sets is developed by using Kalman filter state estimator. Approximately 18 states (from all the 8 inertial measurements) are observed by developing a Kalman filter. A total of five gyroscopes and three accelerometers calibrate the lateral acceleration, yaw velocity of the vehicle and two wheel-sets roll velocity. All the vehicle states are observed by formulating the Kalman filter, moreover, the Kalman filter is also used to calculate the different parameters like cant angle and radius of the curvature of the track. The design of the rail vehicle is validated by comparing the actual results with the same from computer simulations. The simulation also helps to develop an optimal and novel controller to assess the performance of the design of an active steering system.