Abstract
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.
Highlights
Servo systems are widely used in robots, aerospace, computer numerical control machine tools, and other fields [1,2,3]
The results prove that the proposed control strategy is feasible in tracking trapezoidal signal and can achieve a good control performance
We design a reinforcement learning agent to automatically control the parameters of a speed servo system
Summary
Servo systems are widely used in robots, aerospace, computer numerical control machine tools, and other fields [1,2,3]. Their internal electrical characteristics often change, namely, the inertia change of the mechanical structure connected to them and the torque disturbance in the working process. Servo systems generally adopt a fixed-parameter structure proportional–integral–derivative (PID). The control performance of the PID controller with a fixed-parameter structure is closely related to its control parameters. Servo systems often need to reorganize their parameters and seek new control strategies to meet the needs of high-performance control in various situations
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