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In this article, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles with consideration of modeling errors and complex driving scenarios. In order to guarantee operational safety and robustness, a supertwisting second-order sliding mode (SOSM) control strategy is employed to suppress the heavy chattering issue existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy, which combines the SMC algorithm and NDOB technique is proposed based on stability analysis of the corresponding closed-loop system. Finally, hardware-in-the-loop experimental results show that the supertwisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering phenomenon. [ABSTRACT FROM AUTHOR]

In this paper, a general control allocation (CA) algorithm is proposed based on a lexicographic optimization (LO) strategy for a vehicle's longitudinal and lateral control. The primary objective of this CA is to distribute the torque adjustments of the lateral controller such that the error between the desired and actual forces and moments at the vehicle's center of gravity is minimized, while maintaining the longitudinal tire slip ratios close to a desired range. Presence of various constraints in practical systems can significantly increase the complexity and effort of properly adjusting the tuning parameters in the objective function. Hence, an LO method is used to prioritize the objectives. Lateral stability of the vehicle has the highest priority and is subject to the constraint of maintaining small tire slip ratios. The second priority of CA is assigned to minimize the adjustment torques. The proposed LO-based approach reduces exhaustive vehicle testing commonly required for tuning of the separately designed controllers and the cost and time of the implementation. In addition, it makes the algorithm easily transferable from one vehicle to another by reducing the number of tuning parameters. Simulation and experimental results are presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]