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Closed-Loop Operations

A closed-loop control system is most easily understood in the context of an automatic control system (ACS), as it is a specific kind of ACS.

Automatic Control System Model

An automatic control system constitutes an intricate network of interlinked components — external influences, sensors, controllers, and actuators. These components collaboratively operate to guide a system towards a defined state or output.

The four key constituents of the ACS are:

  1. External Influence: Factors affecting system behavior, such as physical quantities or operator commands.
  2. Influence Sensors: Monitor external influence and system response, providing feedback for adjustments or corrections.
  3. Controller: Analyzes input from influence sensors and calculates control actions.
  4. Actuating Device: Executes control actions based on controller input and desired output, performing physical changes.

Closed-Loop system components as ACS

By considering the closed-loop motor driver as a control system, it is possible to identify its key elements and functions.

  1. External Influence: Unpredictable changes in operating load, supply voltage, travel speed, and position.

  2. Influence Sensors: Provide feedback on motor condition and performance.

    • Absolute Magnetic Encoder: Measures motor shaft position and rotation speed.
    • Current Sensor: Monitors motor current consumption dynamics.
    • Accelerometer: Detects motor vibration and system acceleration.
    • Controller: MCU that processes sensor data and adjusts motor driver output accordingly.

    • Actuating Device:

    • Motor Driver: Controls motor operation by regulating its power, speed, direction, and position.

    • Motor: Converts electrical energy into mechanical motion.

Control Algorithm

The closed-loop motor control system strives to sustain optimum motor performance while adhering to necessary operating conditions like torque, position, speed, and motion direction. This process involves the following steps:

  1. The controller receives a target position, speed, or torque from an external source.
  2. The controller obtains sensor data, calculates the discrepancy between the desired and actual motor conditions and performance.
  3. The control algorithm adjusts motor commands to minimize the error and optimize motor operation.
  4. The motor driver implements the updated commands to the motor.
  5. This process continues iteratively, enabling real-time adjustments and optimization until the target conditions are achieved.

Condition Control Algorithm Diagram

Common Error-Correction Strategies

The closed-loop system uses algorithms to correct errors and optimize motor operation. These algorithms include:

  • Proportional-Integral-Derivative (PID) control: Balances the motor's response to errors by adjusting the proportional, integral, and derivative gains.
  • Model Predictive Control (MPC): Predicts future motor behavior based on a mathematical model and optimizes control actions accordingly.
  • Adaptive Control: Adjusts controller parameters in real-time to adapt to changing system dynamics.