关键词: 音圈电机, LabVIEW FPGA, 单神经元自适应控制, RBF神经网络

Abstract: Voice coil motor is a common driver with high dynamic performance. In order to optimize and simplify the debugging process of control algorithms for different voice coil motors, an improved RBF neural network control algorithm based on LabVIEW FPGA was proposed. The network adopted a 3-input three-layer network structure, and updated the weight coefficient of hidden layer by gradient descent method to realize the current loop control of voice coil motor. Simulation result shows that the performance of RBF neural network control algorithm is better than the performance of single neuron adaptive control algorithm when the number of hidden layer nodes increases. When the number of hidden layer nodes is 6, the system current rise time is 14 μs, which is 26.3% quicker than the single neuron adaptive control. The proposed algorithm was applied to the laser precise pointing system and the experimental result shows that the current rise time is 25 μs when there are 4 nodes in the hidden layer. The result is consistent with the simulation results and verifies the effectiveness of the RBF neural network algorithm.

Key words: voice coil motor, LabVIEW FPGA, single neuron adaptive control, RBF neural network