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Research of Parameter Self-learning Fuzzy Control Strategy in motor control system for Electric Vehicles

机译:电动汽车电机控制系统参数自学习模糊控制策略研究

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Based on the vector control method for PMSM, a parameter self-learning hybrid fuzzy controller was implemented to provide the speed control for the EV propulsion system with the purpose to obtain the maximum acceleration during starting and accelerating. A three-term fuzzy controller is implemented by simply using a two-term fuzzy control rule-base without any increase of rules. The method of fuzzy deduction based on phase plane had less computational burden, while the fuzzy inputs could be continuous. The control parameters are self-tuned by introducing a single neuron together with a back-propagation learning algorithm. This method has simpler structure and control algorithms and can be realized online easily. The simulation results and experiment results of 18kW PMSM for electric vehicle propulsion are given, the experiment results show that the electric vehicle with parameter self-learning hybrid fuzzy vector control system has excellent performances of starting, accelerating and cruising on road.
机译:基于PMSM的矢量控制方法,实现了参数自学习混合模糊模糊控制器,为EV推进系统提供了一种目的,以获得开始和加速期间的最大加速度。通过简单地使用两个无模糊控制规则库来实现三术语模糊控制器,而不会增加规则。基于相面的模糊扣除方法具有较少的计算负担,而模糊输入可能是连续的。通过将单个神经元与反向传播学习算法一起引入单个神经元来自调谐。该方法具有更简单的结构和控制算法,可以轻松实现在线。给出了18kW PMSM用于电动车推进的仿真结果和实验结果,实验结果表明,具有参数自学习混合模糊矢量控制系统的电动车辆在道路上的起始,加速和巡航方面具有优异的性能。

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