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Table 7-23. Registers for auto-torque learning routine (continued)

Description

ATQ_LRN_STEP[1:0]

Represents the increment to initial current level. It supports four options -

•

00b : ATQ_LRN_STEP = 128

•

01b : ATQ_LRN_STEP = 16

•

10b : ATQ_LRN_STEP = 32

•

11b : ATQ_LRN_STEP = 64

Example : If ATQ_LRN_STEP = 10b and ATQ_LRN_MIN_CURRENT = 11000b, then -

•

Initial learn current level = 24*8 = 192

•

Final learn current level = 192 + 32 = 224

ATQ_LRN_CYCLE_SELECT[1:0]

Represents the number of electrical half cycles spent in one current level after which the

learning routine allows the current to jump to the other level. It supports four options -

•

00b : 8 half-cycles

•

01b : 16 half-cycles

•

10b : 24 half-cycles

•

11b : 32 half-cycles

LRN_START

Writing 1b to this bit enables the auto-torque learning routine. After learning is completed, this

bit automatically goes to 0b.

LRN_DONE

This bit becomes 1b after learning is complete.

ATQ_LRN_CONST1[10:0]

Indicates the ATQ_LRN parameter at the initial learning current level.

ATQ_LRN_CONST2[10:0]

Indicates the ATQ_LRN parameter at the final learning current level.

VM_SCALE

When this bit is 1b, the auto-torque algorithm automatically adjusts the ATQ_UL, ATQ_LL and

ATQ_LRN parameters as per the supply voltage variation.

Here are few points to consider while setting up the learning routine parameters:

• It is recommended to select the initial current level between 30% to 50% of the maximum operating current.

• Final current level must not exceed 255 and can be selected between 80% and 100% of the maximum

operating current.

• Current waveform distortions (due to high speed or low supply voltage) can cause incorrect reading of the

ATQ_LRN parameters. The learning current levels should be chosen away from the currents where waveform

distortions are observed.

• Low values of ATQ_LRN_CYCLE_SELECT result in quicker learning. However, in systems prone to noise,

higher ATQ_LRN_CYCLE_SELECT can result in more stable ATQ_LRN parameter values.

• Learning should be done when the motor attains the steady-state speed.

• Re-learning should be done if the motor is changed, or the motor speed changes by ± 10%.

For a quick summary, following sequence of commands should be applied to enable automatic learning:

• Write 1b to ATQ_EN

• Run the motor with no load

• Program ATQ_LRN_MIN_CURRENT

• Program ATQ_LRN_STEP

• Program ATQ_LRN_CYCLE_SELECT

• Write 1b to ATQ_LRN_START

• The algorithm runs the motor with initial current level for ATQ_LRN_CYCLE_SELECT number of electrical

half cycles

• Next, the algorithm runs the motor with final current level for ATQ_LRN_CYCLE_SELECT number of

electrical half cycles

• After learning is complete,

– ATQ_LRN_START bit is auto cleared to 0b

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DRV8461

SLOSE98 – DECEMBER 2022

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