SLVAFF1 January   2023 DRV8452 , DRV8462

PRODUCTION DATA  

  1.   Abstract
  2.   Trademarks
  3. 1Power Efficiency of Stepper Motor Drivers
  4. 2Auto-Torque
    1. 2.1 Auto-Torque: Learning Principle
      1. 2.1.1 Configuring Auto-Torque Learning Routine
    2. 2.2 Current Control
      1. 2.2.1 Setting Current Control Parameters
    3. 2.3 PD Control Loop
    4. 2.4 Impact of Auto-Torque Tuning Parameters
      1. 2.4.1 Impact of Learning Parameters on Load Transient Response
      2. 2.4.2 Impact of ATQ_UL, ATQ_LL Hysteresis
      3. 2.4.3 Impact of Load Profile on Power Saving
      4. 2.4.4 Adaptive ATQ_UL, ATQ_LL
      5. 2.4.5 PD Parameter Dependency Curves
        1. 2.4.5.1 Dependency on KP
        2. 2.4.5.2 Dependency on KD and ATQ_D_THR
        3. 2.4.5.3 Dependency on ATQ_FRZ and ATQ_AVG
        4. 2.4.5.4 Dependency on ATQ_ERROR_TRUNCATE
      6. 2.4.6 ATQ_CNT at Different Motor Speeds
      7. 2.4.7 ATQ_CNT at Different Supply Voltages
      8. 2.4.8 Motor Temperature Estimation
    5. 2.5 Efficiency Improvement With Auto-Torque
  5. 3Case Studies
    1. 3.1 Application 1: ATM Machines
      1. 3.1.1 ATM Motor Operating Conditions
      2. 3.1.2 ATM Motor With Auto-Torque
    2. 3.2 Application 2: Textile Machines
      1. 3.2.1 Textile Motor Operating Conditions
      2. 3.2.2 Textile Motor With Auto-Torque
    3. 3.3 Application 3: Printer
      1. 3.3.1 Printer Motor With Auto-Torque
  6. 4Summary
  7. 5References

2.1 Auto-Torque: Learning Principle

This section explains the steps to follow for the auto-torque algorithm to learn about the motor parameters and motor operating conditions.

As mentioned in Section 2, the ATQ_LRN parameter depends upon the constant losses in the system. For any given motor, ATQ_LRN is directly proportional to the coil current. This can be expressed by Equation 3:

Equation 3. A T Q _ L R N =   k × I M V V M

where, IM is the motor current, VVM is the supply voltage to the driver and k is a constant. Equation 3 gives a linear relationship between the ATQ_LRN and the motor current. The auto-torque learning routine learns ATQ_LRN values at any two currents at no load, and then uses this relation to interpolate ATQ_LRN value at any other current.

The ATQ_CNT parameter represents the component of the delivered power that supports the load torque. This relation can be expressed by Equation 4.

Equation 4. A T Q _ C N T =   k 1 × τ × ω I F S

where k1 is a constant at a given operating condition and IFS is the full-scale current (peak of the sinusoidal current waveform) of the stepper driver.

Equation 4 defines the basic working principle of the auto-torque algorithm. The ATQ_CNT parameter can be used to perform motor coil current regulation based on applied load torque on the stepper motor.

Figure 2-2 shows (ATQ_LRN + ATQ_CNT) measured as a function of load torque at 2.5A full-scale current for a hybrid bipolar NEMA 24 stepper motor rated for 2.8A. ATQ_LRN does not change with load torque, whereas ATQ_CNT changes linearly with load torque.

Figure 2-2 (ATQ_LRN + ATQ_CNT) vs. Load Torque