Search Results

This paper presents a simultaneous longitudinal and lateral motion control strategy for a full drive-by-wire autonomous vehicle. A nonlinear model predictive control (NMPC) problem is formulated in which the nonlinear prediction model utilizes a spatial transformation to derive the dynamics of the vehicle about the reference trajectory, which facilitates the acquisition of the tracking errors at varying speeds. A reference speed profile generator is adopted by taking account of the road geometry information, such that the lateral stability is guaranteed and the lane guidance performance is improved. Finally, the nonlinear multi-variable optimization problem is simplified by considering only three motion control efforts, which are strictly confined within a convex set and are readily distributed to the four tires of a full drive-by-wire vehicle.

This paper presents an adaptive observer-based approach for the combined state estimation and active fault detection and isolation (FDI) of the individual-wheel-drive (IWD) vehicles. A 3-DOF vehicle model coupled with the Highway Safety Research Institute (HSRI) tire model is established and used as the observation model. Based on this model, the dual unscented Kalman filter (DUKF) technique is employed for the observer design to give fusion results of the interdependent state and parameter variables, which undergo nonlinear transformations, with the minimum square errors. Effectiveness of the proposed algorithm is examined and validated through co-simulation between MATLAB/Simulink and CarSim. The results demonstrate that the DUKF-based observer effectively filters the sensor signals, accurately obtains the longitudinal and lateral velocities, explicitly isolates the faulty wheel(s) and accurately estimates the actual torque(s) even with the presence of noise.