AM263Px MCU+ SDK: Torque Measurement

| AM263Px MCU+ SDK 26.00.00 |

Torque Measurement

# Introduction

Predicting rotor torque in motors presents a significant engineering challenge: direct measurement requires costly sensors that add weight, a critical concern for electric vehicles striving for efficiency. While traditional statistical methods for estimating rotor torque exist, they are limited by the need for domain expertise and motor-specific tuning.

This project leverages deep learning to efficiently predict rotor torque in Permanent Magnet Synchronous Motors (PMSM), enabling a substantial reduction in the hardware sensors required for motor instrumentation and control.

# Supported Combinations

Parameter | Value
--- | ---
CPU + OS | r5fss0-0 nortos
Toolchain | ti-arm-clang
Board | am263px-lp
Example folder | examples/ai/torque_measurement/

# Dataset and Model Details

## Dataset

The dataset contains sensor measurements collected from a PMSM running on a controlled test bench. The measurements were collected by the LEA department at Paderborn University at 2 Hz sampling rate.

Column Name | Description
--- | ---
`i_d` | Current d-component (active)
`i_q` | Current q-component (reactive)
`u_d` | Voltage d-component (active)
`u_q` | Voltage q-component (reactive)
`motor_speed` | Motor speed
`ambient` | Ambient temperature
`torque` | Torque induced by current (target)

The model uses a subset of easily measurable inputs: Ambient temperature, Coolant temperature, Voltage components (u_d, u_q), and Phase current magnitude to predict torque.

## Model Architecture

This regression model `REGR_10k` contains approximately 10,000 parameters with three convolutional layers (each with BatchNorm and ReLU) followed by two linear layers.

## Input Features

The model takes 4D input (N,C,H,W):

- N (1): batch size which is restricted to 1
- C (10): channels for multiple parameters
- H (128): samples of timeseries data
- W (1): width of samples is restricted to 1 for timeseries applications

## Output

This model produces a 1D output representing the continuous value of torque.

# Feature Extraction Configuration

For this torque measurement system, no FFT or frequency-domain feature extraction is applied. The model uses normalization to convert floating point input into fixed point for efficient inference.

Configuration in `user_input_config.h`:

- SKIP_NORMALIZE: Converts floating point input into fixed point input for the AI model

# Steps to Run the Example

- When using CCS projects to build, import the CCS project for the required combination and build it using the CCS project menu (see Using SDK with CCS Projects).
- When using makefiles to build, note the required combination and build using make command (see Using SDK with Makefiles)
- Launch a CCS debug session and run the executable, see CCS Launch, Load and Run
- The application will predict torque values and compare against expected outputs

# See Also

AI Examples

# Sample Output

Torque Measurement Example Started ...
Feature extraction mismatches 0
All tests have passed!!
Golden vectors matched: 3 not matched: 0