What Is Electric Motor Control?

Electric motor control is the process of regulating speed, torque, position, and other characteristics to enhance performance and optimize energy efficiency. Representing approximately 45% of global electricity consumption, electric motors are integral to traction systems, industrial drives, residential and commercial HVAC setups, and other applications, making electric motor control a key focus.

Conventional motor control algorithms serve as the foundation for the development of electric motor control algorithms, including:

• Scalar control: Also known as volts per hertz (V/f) control, this open-loop electric motor control method maintains a constant voltage-to-frequency ratio in AC motors for basic speed control.
• Field-oriented control (FOC): This closed-loop electric motor control technique employs vector control using Clarke and Park transforms to separate and manage the magnetic and torque-producing components of AC motor currents, enabling precise speed and torque control.
• Direct torque control (DTC): This technique facilitates dynamic torque and flux adjustments without the need for complex transformations, making it suitable for applications requiring quick torque response.

Developing Electric Motor Control Systems with MATLAB and Simulink

With MATLAB^® and Simulink^®, motor control engineers design motor control algorithms, simulate system behavior, and refine performance through real-time testing to determine the suitability of electric motor controller designs and reduce the time and cost of algorithm development before committing to expensive hardware testing.

Developing electric motor control systems involves several activities to go from designing motor control algorithms to implementing those algorithms on a microcontroller or FPGA.

Plant Modeling

Modeling motors and power electronic components is an accepted engineering practice for developing electric motor control systems. Simscape Electrical™ supports multiple fidelity levels in motor modeling, enabling engineers to use simulation to select the appropriate level of detail for various applications, such as motor sizing and electric vehicle traction motor control design.

Algorithm Development

Designing and refining control algorithms enables precise management of speed, torque, and energy consumption in electric motors. Motor Control Blockset provides optimized prebuilt blocks and reference examples to accelerate the development and deployment of sensored and sensorless motor control techniques.

Controller Tuning

Achieving the desired system behavior involves optimizing controller parameters. Simulink Control Design™ provides tools such as FOC Autotuner, PID autotuning, and frequency response estimation, facilitating efficient tuning and performance optimization of system response and stability.

Code Generation

Code generation transforms verified control algorithms into executable code for deployment on hardware. You can leverage rapid control prototyping and hardware-in-the-loop (HIL) simulations on a real-time target by generating C, C++, or HDL code for motor control algorithms to validate motor controllers. Embedded Coder^® generates optimized C and C++ code from Simulink models, with hardware support packages facilitating automatic deployment to C2000, STM32, Infineon Aurix, and other MCUs. For targeting FPGAs and SoCs, HDL Coder™ facilitates code generation and deployment on Intel, Xilinx, and Microchip devices.

Electric Motor Control Applications

Reference examples with Motor Control Blockset™ also cover advanced motor control strategies such as model predictive control, active disturbance rejection control, and reinforcement learning to accelerate your development process.

Model Predictive Control

Model predictive control (MPC) enhances field-oriented control over PID by effectively managing constraints such as torque saturation and voltage limits while addressing cross-coupling between loops to optimize control goals. You can evaluate the MPC controller by simulating it in a closed loop with the motor plant in Simulink using the reference example.

Active Disturbance Rejection Control

To improve system robustness, you can use active disturbance rejection control (ADRC) to compensate for disturbances and ensure stability under variable conditions. While PID tuning requires significant effort, ADRC provides a nonlinear control solution that achieves good performance with a simpler setup and less tuning effort. The Active Disturbance Rejection Control block simplifies the implementation of ADRC, making it easier for beginners to apply the technique.

Reinforcement Learning

Reinforcement learning provides a dynamic approach to control by learning from interactions with the environment, making it a strong alternative to traditional controllers such as PID, especially when it’s difficult to characterize motors and their operating conditions. Despite its potential, reinforcement learning requires significant computational resources and training time. In Simulink, the Reinforcement Learning Agent block facilitates the configuration of a reinforcement learning controller for field-oriented control,  enabling engineers to leverage this advanced technique.

By leveraging prebuilt blocks and reference examples with Simulink, engineers can develop control strategies and accurate motor models to effectively address the challenges of building electric motor control systems.