Control (management)

Temperature control

Process (computing)

Engineering

Servo control

Control system

Servomotor

Control engineering

Operating system

Computer science

Classical mechanics

Thermodynamics

Physics

Torque

PID controller

Reinforcement learning

Inertia

Artificial intelligence

Moment (physics)

Structural engineering

Control theory (sociology)

Servo

Servomechanism

Reinforcement

Electrical engineering

Algorithms

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.

Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning