This paper aims to achieve precise motion control of linear push rods, and the radial basis function (RBF) neural network to adjust parameters of the Proportional Integral Derivative (PID) controller is introduced in this paper. The proposed RBF-PID control strategy is used for the motion control of linear push rods, and the validation experiments are carried out.
First, the structure and working principle of the linear push rods were analyzed, and the hardware system of the control system was designed based on the pulse width modulation principle. Subsequently, based on the discrete PID algorithm, an RBF-PID control scheme for linear push rods was proposed by combining RBF neural networks. Finally, the effectiveness of the proposed scheme was verified by comparing the motion control experiments of sine wave and composite waveform.
The experimental results show that the accuracy of linear push rod trajectory tracking control using the RBF-PID control method is higher than that of traditional PID control, and it has good anti-interference and robustness. Compared with the traditional PID controller, the RBF-PID controller reduces the maximum error (MAX), root-mean-square error (RMSE) and mean absolute error (MAE) by 11.73%, 11.5% and 13.57%, respectively, when tracking sine signals; when tracking composite wave signals, the RBF-PID controller reduced MAX, RMSE and MAE by 31.44%, 3.99% and 13.67%, respectively.
This paper proposes a novel RBF-PID control approach for the motion control of linear push rods, and the experimental results have demonstrated the effectiveness of this method, which can further extend the application of linear push rods.
