The first practical use of an electric motor was recorded in 1834 by Thomas Davenport to power a railway car on a short section of track.
Today, motors are the prime motivator in electrified transportation, industrial automation, and commercial and consumer products. A study from the International Energy Agency (IEA) estimates that 40–45% of the world’s generated electricity is consumed by systems driven by motors.
In recent decades, brushless motors became more popular due to their higher efficiency, power density, and reliability. As brushless motors became popular, control techniques were developed to bring about precise control for these motors and further improve their efficiency.
Field-oriented control (FOC) is one such control technique that provides precise control over the full range of torque and speed for brushless motors. Field-oriented control relies on PI controllers for speed, Iq, and Id control loops. PI controllers are simple and easy to implement but might be challenging to tune in situations where there are uncertainties and external disturbances present.
Some examples are:
• Uncertainties in motor parameters and system dynamics
• Changes in motor parameters (resistance, inductance, back EMF, etc.) with wear, aging, and operating temperature
• Load torque and input voltage fluctuations
• Changes in operating region and hysteresis in motor behavior
Apart from accounting for these factors, one must also consider the need to retune controllers if motors are resized for your application. This process entails significant effort. To address these challenges, advanced control algorithms can be used to design field-oriented controllers that can account for these factors while improving motor control accuracy, response time, and efficiency even in challenging environments.
Apart from accounting for these factors, one must also consider the need to retune controllers if motors are resized for your application. This process entails significant effort. To address these challenges, advanced control algorithms can be used to design field-oriented controllers that can account for these factors while improving motor control accuracy, response time, and efficiency even in challenging environments.
In this white paper from MathWorks, developer of MATLAB and Simulink software, you will have an understanding of designing field-oriented controllers. The paper will discuss the appropriate tools in MATLAB® and Simulink® to use when working with the following control techniques:
• Active disturbance rejection control (ADRC)
• Model predictive control (MPC)
• Reinforcement learning (RL)
In summary, this white paper discussed alternative control strategies for field-oriented controllers in electric motors, focusing on active disturbance rejection control, model predictive control, and reinforcement learning. These advanced control techniques offer improved motor control accuracy, response time, and efficiency, even in challenging environments.
MATLAB, Simulink, and associated toolboxes from MathWorks provide an accessible platform to design and implement these advanced control techniques for motor control applications. However, it is essential to consider the tradeoffs of computational complexity, real-time implementation, and data requirements when selecting an appropriate control strategy for a specific application.
