基于复合神经网络重构对象的永磁同步直线电机变参数型位移速度并行控制

doi:10.19595/j.cnki.1000-6753.tces.230104

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Abstract A permanent magnet synchronous linear servo motor has the characteristics of high thrust density, high efficiency, and fast control response. It is widely used in rail transit, intelligent manufacturing, and aerospace fields. The electrical or mechanical parameters of the motor and nonlinear resistance interference will affect the high precision control of the linear servo. Therefore, the stable and efficient displacement control algorithm has important engineering application value to improve the system performance. Servo control can be divided into serial and parallel systems according to the relative position of displacement and velocity loop. However, whether serial or parallel control, most controller parameters need to be set in advance and are generally fixed, and adjusting the actual control effect is impossible. This paper proposes a parallel control strategy of variable parameter displacement velocity of permanent magnet synchronous linear motor based on composite neural network reconstruction object, which realizes the high-performance control of linear servo motor.
Firstly, a parallel controller with variable parameters is designed using the error information of the moving displacement and linear velocity. Then, the displacement output of the permanent magnet synchronous linear motor is reconstructed by a composite radial basis neural network containing multi-dimensional information of the control object, and the partial derivative of the displacement to the control signal is obtained. Finally, based on the closed-loop stability condition of the system, a complete parameter update strategy of parallel controller with displacement velocity is provided according to the comparison between periodic retrieval errors and control targets.
In the experiment, a permanent magnet synchronous linear servo system is designed, and different network observations, position controls, and object parameter controls are compared. The network comparison shows that the composite radial-based neural network has fast observation speed and high observation accuracy, and the observation time and root mean square error are 18.63 μs and 0.063 mm, respectively. The position control comparison shows that the variable parameter parallel algorithm has higher control precision than the fixed parameter algorithm, such as PID control, with a 30%~50% improvement effect in the displacement and velocity control error indexes. The parameter control comparison shows that in the variable parameter parallel control strategy, when the moving mass changes, the root mean square of displacement error is less than 0.015 mm, and the absolute maximum error is less than 0.060 mm, which means that the algorithm has stability and universality.
The following conclusions can be drawn: (1) The composite RBF neural network structure has better observation performance than the traditional RBF neural network. Compared with the traditional improvement method, it has a better effect with less computing pressure on the processor. (2) Compared with the fixed coefficient algorithm, such as PID, the proposed variable parameter parallel control strategy can effectively improve the control performance of the actuator displacement. (3) The variable parameter parallel control strategy can guarantee the high precision control effect under different displacement settings and object parameters.

Key words： Permanent magnet synchronous linear motor parallel control composite radial basis function neural network variable parameters update mechanism

Received: 04 January 2023

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TM359.4

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	Zhou Yangzhong

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Bao Mingkun,Zhou Yangzhong. Parallel Displacement Velocity Control of Permanent Magnet Synchronous Linear Motor with Variable Parameters Based on Composite Neural Network Reconstruction Object[J]. Transactions of China Electrotechnical Society, 2024, 39(8): 2470-2484.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230104 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I8/2470

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