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The linear wheel model is the most widely used in ride comfort analysis of tracked vehicles. However, as the speed of the vehicle increases and the application of new materials, and the road wheel becomes lighter and softer, its nonlinear characteristics become more and more obvious and can't be ignored in vehicle dynamics simulation. This paper aims to study the effect of nonlinear stiffness of a novel flexible road wheel (FRW) with unique suspension bearing structure on the ride comfort of a tracked vehicle traversing random uneven road. The linear and nonlinear models of FRW were established by fitting the load-deflection data obtained from the static loading test of the physical prototype. The established linear and nonlinear models of FRW were added to a half-vehicle model of a tracked vehicle which has been proved by published test results. The ride comfort of the half-vehicle model of the tracked vehicle with linear and nonlinear models of FRW on random uneven road surface was studied in detail. The study results show that, compared with the linear wheel model, the nonlinear model has a tremendous influence on the dynamic response of the tracked vehicle, effectively suppressing the vibration, especially for the high frequency excitation. In addition, the nonlinear factor has a greater impact on the dynamic performance of the wheel than on the suspension and the body. The research results enrich the study of nonlinear dynamics of tracked vehicles, and provide reference for nonlinear modeling of other pneumatic tires and non-inflatable wheels.INDEX TERMS Ride comfort, flexible road wheel, wheel stiffness test, nonlinear wheel model, Matlab/Simulink.

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The adaptive cruise control (ACC) system has received significant attention due to traffic safety improvement, traffic throughput increment, and energy conservation. Model Predictive Control (MPC) has been successfully applied in the control of multi-objective vehicular ACC. However, as a state-feedback policy, MPC requires full state measurement. Meanwhile, the real-time performance of MPC is intractable. This paper proposes to estimate the state value and disturbance value with an extended state Kalman filter to deal with measurement uncertainty. The Kalman filter is based on an augmented state-space model which takes the disturbance term as a new state. To improve real-time performance, this paper suggests employing an explicit MPC (EMPC) based on binary search tree to move the online computational burden of MPC to offline computation by multi-parametric quadratic programming (MPQP). An improved algorithm to solve the MPQP problem offline is proposed, which is initialized discarding the requirement of parameters range, while previous methods need. In the simulated measurement process, the extended state Kalman filter can effectively reduce noise and accurately estimate the value of state and disturbance in the car-following model. Simulations in different scenarios are performed to test the effectiveness of the proposed ACC controller. Results show that the proposed EMPC for the ACC system can improve the real-time performance of the MPC with little loss of performance. On average, the EMPC via binary search is 95.8 times faster than the MPC with the same parameters as EMPC for the studied ACC system. And it has better overall performance compared with the ACC with collision avoidance (CA-ACC) method.

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