A robust adaptive anti-slip regulation controller which consists of two components, namely a road friction coefficient estimator and a wheel dynamics controller, is designed for distributed-drive electric vehicles. The road friction coefficient estimator is based on the latest non-affine parameter estimation theory to achieve the peak road friction coefficient. Also, working conditions for the road friction coefficient estimator are proposed to avoid the estimation error caused by a small slip ratio. According to the results of the road friction coefficient estimator, the desired reference slip ratio is obtained and the key parameters of the robust adaptive anti-slip regulation controller are modified to make sure that the road conditions can be made full use of. Then, according to the desired reference slip ratio, a state feedback control law with a conditional integrator is designed on the basis of the Lyapunov stability theory for a wheel dynamics controller by analysis of the non-linear characteristics of the tyres and the driver’s intended driving torque and constraints from the ground–tyre adhesion. In addition, it achieves smooth switching between optimal driving and the driver’s intended driving torque rather than normal switching logic. Multi-condition simulations and experiments show that the controller is adaptive to different road conditions, can improve the driving efficiency of the vehicle and can ensure stability of the vehicle. Finally, with comparative experiments, the distributed-drive electric vehicle with a robust adaptive anti-slip regulation controller proves to be more efficient than the traditional vehicle with a traditional anti-slip regulation controller.


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    Title :

    Robust adaptive anti-slip regulation controller for a distributed-drive electric vehicle considering the driver’s intended driving torque


    Contributors:
    Yu, Zhuoping (author) / Zhang, Renxie (author) / Lu, Xiong (author) / Jin, Chi (author) / Sun, Kai (author)


    Publication date :

    2018-03-01


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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