Motor control algorithms regulate speed, torque, and other performance characteristics, often for precision positioning. Evaluating control algorithms using simulation is an effective way to determine the suitability of motor controller designs and reduce the time and cost of algorithm development before committing to expensive hardware testing.

An efficient workflow for motor control algorithm development involves:

• Building accurate system models, often from libraries of motors, drive electronics, sensors, and loads
• Generating ANSI, ISO, or processor-optimized C code and HDL for real-time testing and implementation
• Verifying and testing control algorithms using simulation and prototyping hardware.

For details on simulation, see Simulink^®. To generate ANSI, ISO, or processor-optimized C code to implement motor control algorithms on processors, or generate HDL for FPGAs, see Embedded Coder^® and HDL Coder™.

View this video to learn how to apply Reinforcement Learning for field-oriented control of a permanent magnet synchronous motor (6:12).