Motor Current Control Using Reinforcement Learning to Cut Overshoot
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Solution Overview
Problem
Existing electric motor control systems face challenges with overshoot in PID controllers, time-consuming parameter tuning, and high tracking errors for rotational speed and current.
Innovation Solution
A processor and motor control device utilizing a reinforcement learning controller and algorithm within the PID controller's current loop, combined with a PDFF controller in the speed loop, to improve control performance by reducing overshoot and parameter tuning time, and adjusting transient response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If PID controller is used for motor control, then basic control functionality is achieved, but overshoot occurs and parameter tuning is time-consuming
Solution Approach 1:
The reinforcement learning controller enables the system to automatically tune its own parameters through continuous learning from operational data. The controller self-adjusts control parameters without external intervention, transforming the manual parameter tuning process into an autonomous self-optimization process that eliminates time-consuming manual adjustments
Solution Approach 2:
The system implements continuous feedback mechanisms where the reinforcement learning controller receives real-time performance data from sensors monitoring motor speed, current, and other parameters. This feedback loop enables the controller to learn from actual system behavior and dynamically adjust parameters to prevent overshoot and optimize performance
2Measurement precision
If PID controller is used for motor control, then basic control is achieved, but tracking error of rotational speed and current is high
Solution Approach 1:
The control system transitions from static PID parameters to dynamic parameter adjustment through reinforcement learning. The controller continuously adapts its control strategy based on real-time system state, enabling it to respond dynamically to changing load conditions, speed variations, and motor characteristics, thereby minimizing tracking errors across different operating conditions
Solution Approach 2:
The reinforcement learning controller dynamically changes control parameters based on system state and learned patterns. Instead of fixed PID parameters, the system adjusts proportional, integral, and derivative gains along with additional control variables in real-time, enabling precise tracking of rotational speed and current by adapting parameters to match actual operating conditions
3Adaptability or versatility
If traditional motor control method is used, then simple control structure is maintained, but control performance cannot be optimized for different motor specifications and torque load changes
Solution Approach 1:
The reinforcement learning controller serves multiple functions within a single control architecture. It performs parameter tuning, overshoot prevention, tracking error minimization, and adaptation to different motor specifications simultaneously. This multi-functional approach enables the same control structure to handle diverse motor types and operating conditions without requiring separate control systems for each application
Solution Approach 2:
The controller automatically adapts to different motor specifications through self-learning from operational data. When a new motor is installed or operating conditions change, the reinforcement learning controller autonomously tunes its parameters and control strategy without requiring manual reconfiguration or complex setup procedures, enabling universal applicability across different motor types
Data Source
AI summary
A processor for controlling a motor, a motor control device and a control method therefore are provided. The processor includes a feedback calculator, a control calculator and a drive calculator. The feedback calculator calculates a direct-axis current and a quadrature-axis current according to a drive current driving a motor and an operating angle of the motor. The control calculator includes a reinforcement learning controller. The reinforcement learning controller uses a reinforcement learning algorithm to calculate a direct-axis voltage and a quadrature-axis voltage according to a quadrature-axis current command, the direct-axis current and the quadrature-axis current. The quadrature-axis current command is obtained according to a reference rotational speed and the operating speed of the motor. The drive calculator generates a switching signal according to the direct-axis voltage, quadrature-axis voltage and an operating angle of the motor. The switching signal is used to control a driving circuit to drive the motor.