Abstract
For the DC servos that removes the control electronics with certain load, the efficiency of establishing the controller model is improved by establishing the steering gear control model to achieve the simulated control effect on the motor. Based on Tensorflow, the control model of the DC servo is established. The input of the model is the angular state of the motor and different expected angles. The output of the model is the feedback corresponding to the motor. Then based on the control model of the servo, the controller model of the PID neural network is established. The input of the controller model is the expected angle and the angular position of the motor, and the output is the pulse width of the PWM. Adaptive control of the DC servo is confirmed by the controller model.
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Acknowledgments
This study was supported by ‘Study on Key technologies of Parallel Robot for Minimally Invasive Spine Surgery’, Scientific Research Project of Shanghai Municipal Science and Technology Commission, (Projection No. 16090503700).
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Jing, X., Cheng, K. (2020). Adaptive Control of DC Servo Based on PID Neural Network. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_14
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DOI: https://doi.org/10.1007/978-981-32-9698-5_14
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