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Adaptive intelligent speed control of switched reluctance motors with torque ripple reduction

机译：降低转矩脉动的开关磁阻电动机的自适应智能速度控制

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摘要

Switched reluctance (SR) motors have a wide range of applications in industries, mainly due to the special properties of this motor. However, because of its dynamical nonlinearities, its control is complex. This paper presents an adaptive intelligent control based on the Lyapunov stability theory to control the speed of SR motors with good accuracies and performances. The proposed controller is composed of a speed controller and a torque controller. The main parts of the speed controller are two-fold: (a) the optimal controller, which is based on the Hamilton-Jacobi-Bellman theory and (b) the intelligent controller, which is an adaptive fuzzy controller. The main features of the proposed speed controller are: (1) its independence of the exact parameters of the SR motor such as the inertia of rotor, the viscous friction and the load torque and (2) the robustness to inaccuracies and disturbances. Moreover, the torque ripple reduction is achieved by employing a neural network for torque estimation. The simulation results show good performance of the proposed controller in speed controlling and torque ripple reduction.
机译：开关磁阻（SR）电动机在工业上具有广泛的应用，这主要是由于该电动机的特殊性能。但是，由于其动态非线性，其控制非常复杂。本文提出了一种基于李雅普诺夫稳定性理论的自适应智能控制，以具有良好的精度和性能来控制SR电动机的速度。所提出的控制器由速度控制器和转矩控制器组成。速度控制器的主要部分有两个方面：（a）基于汉密尔顿-雅各比-贝尔曼理论的最优控制器，以及（b）作为自适应模糊控制器的智能控制器。所提出的速度控制器的主要特征是：（1）它与SR电动机的精确参数（例如转子的惯性，粘滞摩擦和负载转矩）无关，以及（2）对不精确和干扰的鲁棒性。此外，通过使用神经网络进行扭矩估计来实现扭矩波动的减小。仿真结果表明该控制器在速度控制和转矩脉动减小方面具有良好的性能。

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