永磁球形电动机动力学解耦控制及通电策略研究
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摘要
针对一种新型永磁球形电动机,本文讨论了其主要结构特征,并对该电动机进行了动力学建模,分析了其模型误差及产生原因。根据动力学模型中存在大量非线性耦合项的特点,本文提出了一种基于模糊控制器的动力学解耦控制算法;随后,为消除动力学模型中的不确定性,进一步改善解耦控制效果,又提出了一种基于神经网络辨识器和ANFIS的动力学解耦控制算法。这两种控制算法均采用计算力矩法的结构,能消除各欧拉角轴向之间的非线性交叉耦合,提高系统的动态和静态性能。仿真分析验证了提出算法的正确性。
本文提出了一种基于球面规划的定子绕组通电策略。首先,用有限元法得到了永磁球形电动机的静态转矩模型。然后,将定子球面上定子绕组覆盖区域进行球面规划,划分为四类子区域,用解析法对所有子区域进行描述,并根据相似三角形原理对各个定子绕组进行标号。最后,根据转子的位置,用特定的法则确定需要通电的定子绕组,并调用静态转矩模型,求解得到通电电流大小。采用该通电策略后,永磁球形电动机能在不同控制算法下实现复杂的轨迹跟踪运行。仿真分析验证了该通电策略的合理性。
对于永磁球形电动机逆运动学问题来说,解析求解较为复杂。针对此问题,本文提出了一种基于神经网络的逆运动学求解方法,用神经网络来逼近该电动机的逆运动学模型;根据神经网络采用L-M优化算法时的样本学习效果,确定了此神经网络的最优结构。随后,本文又提出了一种基于改进蚁群算法的逆运动学求解方法,验证了该算法的优越性,并对其参数设置进行了研究。
本文搭建了基于DSP TMS320F2812的永磁球形电动机双闭环自转调速实验平台,并对硬件电路的各主要部分进行了讨论。通过对永磁球形电动机的转子位置和母线电流的检测,实现了其双闭环调速控制。从调速效果可以看出,永磁球形电动机能够实现较快的启动和平稳的运行。
One kind of Permanent Magnet Spherical Motor (PMSM) is proposed in this paper, and the mechanical structure of each part is discussed first. Dynamical modeling of PMSM is made, and model errors, including structured uncertainties and unstructured uncertainties, are analyzed. According to the fact that there exist lots of inter-axis nonlinear couplings in its dynamic model, dynamic decoupling algorithms based on fuzzy controllers is proposed. In order to eliminate the effects of inter-axis couplings in the dynamic model, the dynamic decoupling algorithm with neural network identifier and ANFIS is proposed then. The two proposed algorithms, in which Computed Torque Method (CTM) structure is employed, can eliminate the couplings and improve the static and dynamic performances of the control system. Simulations verify the effectiveness of the proposed algorithms.
A spherical planning based electrifying strategy of PMSM is proposed. First, the Finite Elements Method (FEM) is employed to obtain the static torque model of PMSM. Then the region with stator coils shaded on Stator Spherical Surface is spherically planned and divided into four classes of sub-regions, which can be analytically expressed. All stator coils are labeled according to similar triangle principle. Finally, static torque model is employed and linear equations are solved to obtain the electrifying stator coils and the electrifying currents. PMSM can realize complex trajectory tracking operations under different algorithms with the proposed electrifying strategy applied. The simulations are made, and the effectiveness of the proposed electrifying strategy is verified.
With regards to the inverse kinematics problem of PMSM, analytically solving methods are complicated relatively. Solving methods of inverse kinematics based on feed forward neural network and that based on Advanced Ant Colony Algorithm (AACA) is proposed separately. Through the study of training data, neural network can approach the inverse model. According to the learn effects of training data with L-M algorithm applied, the optimum structure of the neural network for the inverse kinematics problem is determined. The AACA is then proposed to solve the inverse kinematics problem. the advantages of AACA are verified, and the parameters’configurations of AACA are discussed according to the solving effects.
According to the mechanical structure of PMSM, each main part of hardware circuit are discussed, and the hardware experiment platform based on TMS320F2812 DSP is built. Through the detection of rotor’s position and bus current, the hardware platform can realize two closed-loops speed regulations with rapid start and stable operation.
本文提出了一种基于球面规划的定子绕组通电策略。首先,用有限元法得到了永磁球形电动机的静态转矩模型。然后,将定子球面上定子绕组覆盖区域进行球面规划,划分为四类子区域,用解析法对所有子区域进行描述,并根据相似三角形原理对各个定子绕组进行标号。最后,根据转子的位置,用特定的法则确定需要通电的定子绕组,并调用静态转矩模型,求解得到通电电流大小。采用该通电策略后,永磁球形电动机能在不同控制算法下实现复杂的轨迹跟踪运行。仿真分析验证了该通电策略的合理性。
对于永磁球形电动机逆运动学问题来说,解析求解较为复杂。针对此问题,本文提出了一种基于神经网络的逆运动学求解方法,用神经网络来逼近该电动机的逆运动学模型;根据神经网络采用L-M优化算法时的样本学习效果,确定了此神经网络的最优结构。随后,本文又提出了一种基于改进蚁群算法的逆运动学求解方法,验证了该算法的优越性,并对其参数设置进行了研究。
本文搭建了基于DSP TMS320F2812的永磁球形电动机双闭环自转调速实验平台,并对硬件电路的各主要部分进行了讨论。通过对永磁球形电动机的转子位置和母线电流的检测,实现了其双闭环调速控制。从调速效果可以看出,永磁球形电动机能够实现较快的启动和平稳的运行。
One kind of Permanent Magnet Spherical Motor (PMSM) is proposed in this paper, and the mechanical structure of each part is discussed first. Dynamical modeling of PMSM is made, and model errors, including structured uncertainties and unstructured uncertainties, are analyzed. According to the fact that there exist lots of inter-axis nonlinear couplings in its dynamic model, dynamic decoupling algorithms based on fuzzy controllers is proposed. In order to eliminate the effects of inter-axis couplings in the dynamic model, the dynamic decoupling algorithm with neural network identifier and ANFIS is proposed then. The two proposed algorithms, in which Computed Torque Method (CTM) structure is employed, can eliminate the couplings and improve the static and dynamic performances of the control system. Simulations verify the effectiveness of the proposed algorithms.
A spherical planning based electrifying strategy of PMSM is proposed. First, the Finite Elements Method (FEM) is employed to obtain the static torque model of PMSM. Then the region with stator coils shaded on Stator Spherical Surface is spherically planned and divided into four classes of sub-regions, which can be analytically expressed. All stator coils are labeled according to similar triangle principle. Finally, static torque model is employed and linear equations are solved to obtain the electrifying stator coils and the electrifying currents. PMSM can realize complex trajectory tracking operations under different algorithms with the proposed electrifying strategy applied. The simulations are made, and the effectiveness of the proposed electrifying strategy is verified.
With regards to the inverse kinematics problem of PMSM, analytically solving methods are complicated relatively. Solving methods of inverse kinematics based on feed forward neural network and that based on Advanced Ant Colony Algorithm (AACA) is proposed separately. Through the study of training data, neural network can approach the inverse model. According to the learn effects of training data with L-M algorithm applied, the optimum structure of the neural network for the inverse kinematics problem is determined. The AACA is then proposed to solve the inverse kinematics problem. the advantages of AACA are verified, and the parameters’configurations of AACA are discussed according to the solving effects.
According to the mechanical structure of PMSM, each main part of hardware circuit are discussed, and the hardware experiment platform based on TMS320F2812 DSP is built. Through the detection of rotor’s position and bus current, the hardware platform can realize two closed-loops speed regulations with rapid start and stable operation.
引文
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