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

3.2.2 Textile Motor With Auto-Torque

In this application, the stepper motor was subjected to load torque transients between 50 mNm and 1.5 Nm at a fast rate of 1.5 Nm/15ms. The on time for the peak load was roughly 1 s, and the duration between peak load events was 4 s, corresponding to a 20% duty cycle for the peak load.

Figure 3-6 shows the snapshot of the learning routine for this motor.

The auto torque learning routine was run at no load with the following parameter values:

  • ATQ_LRN_MIN_CURRENT = 1011000b
  • ATQ_LRN_STEP = 00b

Values for the ATQ_LRN parameters are:

  • ATQ_LRN_CONST1 = 43
  • ATQ_LRN_CONST2 = 99
GUID-20221117-SS0I-JMP3-MNJX-P4HT0J0BLQLT-low.png Figure 3-6 Learning Routine Snapshot for Textile Motor

The parameters for current control and PD loop control were selected as:

  • ATQ_TRQ_MAX = 216
  • ATQ_TRQ_MIN = 80
  • ATQ_UL = 13
  • ATQ_LL = 12
  • KP = 1
  • KD = 15
  • ATQ_D_THR = 7
  • ATQ_ERROR_TRUNCATE = 0
  • ATQ_FRZ = 1
  • ATQ_AVG = 0

Figure 3-7 to Figure 3-10 showcase the output current and supply current waveforms with and without auto-torque in the event of a load torque change. As is expected, supply current consumption is significantly lower with auto-torque.

GUID-20221117-SS0I-LXMM-BZDG-KMH6QFRJQDQ6-low.png Figure 3-7 Textile Motor Loading/Unloading Without Auto-Torque
GUID-20221117-SS0I-FXSV-F4XL-FWQG7MSMWFK1-low.png Figure 3-8 Textile Motor Loading/Unloading Without Auto-Torque
GUID-20221117-SS0I-ZWKH-1NPJ-1X2TSZPFFPWH-low.png Figure 3-9 Textile Motor Loading/Unloading With Auto-Torque
GUID-20221117-SS0I-JWLG-5N7Q-WRJ1TTKKQ88L-low.png Figure 3-10 Textile Motor Loading/Unloading With Auto-Torque

Based on lab measurements:

  • Power consumed without auto-torque = 24 V * 2.345 A = 56.28 W
  • Power consumed with auto-torque = 24 V * 960 mA = 23.04 W
  • This represents a power saving of 59 %.
  • Power loss in the motor without auto-torque = (9 A * 9A * 0.15 Ω) = 12.15 W
  • Power Loss in the motor with auto-torque = (0.2 * 9 A * 9 A + 0.8 * 3.6 A * 3.6 A) * 0.15 Ω = 4 W
  • This corresponds to a heat reduction of 67 % in the motor coils.