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Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning

School of Electrical Engineering and Automation, East China Jiaotong University, Nanchang 330013, China
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Algorithms 2018, 11(5), 65; https://doi.org/10.3390/a11050065
Received: 8 March 2018 / Revised: 28 April 2018 / Accepted: 3 May 2018 / Published: 5 May 2018
(This article belongs to the Special Issue Algorithms for PID Controller)
We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional–integral–derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance. View Full-Text
Keywords: servo system; deep reinforcement learning; PID parameter tuning; torque disturbance; inertia change servo system; deep reinforcement learning; PID parameter tuning; torque disturbance; inertia change
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Chen, P.; He, Z.; Chen, C.; Xu, J. Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning. Algorithms 2018, 11, 65.

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