The key problems of the steering system and vehicle handling stability of distributed drive electric vehicles are studied in the paper. According to the performance of the distributed drive electric vehicle, a seven-degree-of-freedom is established to match the parameters with the model of CarSim software. Based on the maneuverability improvement control algorithm of the active front wheel system [Active Front Steering (AFS)]/differential power steering system coupling redundancy between differential drive assist steering (DDAS), a AFS/DDAS system coordination control strategy is formulated to reduce the influence of undesirable cross-pendulum moments on vehicle stability. Through the requirements for vehicle stability, a distributed control strategy for the steering stratification of electric vehicles is developed. In the upper layer, the direct transverse moment controller is designed by the linear quadratic regulator algorithm, and the stability control area is divided into a stable region, a transition region, and an unstable region according to the center-of-mass deviation angle. Then the weights of the three subsystems are determined by judging the control area of the vehicle. The lower controller distributes the additional torque from the final calculation of the upper controller to the driving wheels through an allocation algorithm. Finally, using the CarSim/Simulink simulation platform and the dSPACE hardware-in-the-loop real-time simulation platform, the effectiveness and real-time performance of the steering coordination control strategy are verified by simulation experiments under typical working conditions, such as double shift line, step, and sinusoid.
Focusing on the driving force distribution control method of four-wheel independent drive electric vehicles, a driving force distribution strategy with a hierarchical structure is proposed in this paper. The upper layer used a neural network to optimize the fuzzy rules and membership function so as to adapt to changes in the process parameters and subjectivity owing to the limitations of the fuzzy control rules acquired by human experience. The lower layer used the optimal distribution method based on rules, focusing on the minimum tire load ratio. The proposed control strategy was verified by Carsim/Simulink co-simulation and a hardware-in-the-loop experiment. The results show that the proposed control strategy can improve the handling stability of vehicles on roads with different levels of adhesion.
To effectively predict the peak of reversal error of tilt feed system and reduce reversal error caused by friction and gravity components, a peak prediction method of reversal error for tilt feed system on the precision NC machine tool is proposed. According to the load, tilt angle, motion trajectory, maximum static friction torque and relevant dynamic characteristic information, the peak prediction formula of the reversal error for the tilt feed system is established by mathematical derivation based on the kinematics, dynamics and torque balance during the process of reversal. Thus, the peak of reversal error for the tilt feed system can be obtained. The experimental results show that this method can achieve a good prediction effect, and can predict the peak of reversal error before the machining. It provides a theoretical basis for the reversal error suppression.
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