How AI is Promising an Electric Motor Calibration Breakthrough

Modern motor control typically relies on a feedback loop, most commonly a PI or PID controller. In theory, it’s elegant. In practice, it’s painful. Here’s what tuning a motor usually involves:

1. Install the motor into its real system
   Need to carefully measure system load. Gearboxes, belts, inertia, friction, and compliance all change how the motor behaves.
2. Inject test signals
   Engineers run step inputs, ramps, or chirps to see how the motor responds.
3. Measure tracking error
   Determine how far off the motor is from where it should be.
4. Manually adjust gains
   In layman terms, gain adjustment means deciding how hard the motor controller should adapt when the motor makes a mistake. Getting this balance right is what takes experts hours.
5. Repeat, many times
   Change the load? Repeat. Change the speed profile? Repeat. Temperature drift? Repeat.

Even when done well, PI control struggles with nonlinearities like friction, saturation, dead zones, and multi-inertia dynamics. To compensate, engineers often add feedforward control, which tries to predict the torque or current required to follow a motion command before errors even occur. But feedforward has its own problem: it requires accurate models of the motor and the mechanical system it powers. And in the real world, those models are rarely accurate for long. This is why motor tuning remains a hands-on, expert-driven process. It also means that tuning is a slow and expensive undertaking that scales poorly.

Machine learning has been applied to motor control before, but with major limitations. Some approaches require collecting large datasets for each motor and load condition. Others rely on retraining models whenever the system changes. Reinforcement learning methods can work, but they often need thousands of trials, can suffer from sim-to-real gaps, and can be risky to test on physical hardware. In short, most AI approaches trade one kind of cost, such as manual tuning, for another: data collection, retraining time, or system complexity. The missing piece has been generalization: the ability to understand a new motor from almost no data.

In-context learning changes the rules.

Instead of updating model parameters, an in-context learning model adapts by referencing examples. You show it a single (input, output) pair, and it infers the underlying system behavior implicitly. This idea has been explored extensively in language models. For the first time ADI is demonstrating the same principle works for real-world motor control. Rather than asking the AI to become a new controller through training, you ask it to recognize the motor it’s seeing and respond accordingly.

The approach is surprisingly elegant and happens in two stages.

Step 1: Learn the Language of Systems

Before touching a real motor, the model is pretrained on tens of thousands of synthetic systems, both linear and nonlinear. These systems include behaviors like saturation, friction, and dead zones that commonly appear in real electromechanical setups. From this data, the model learns a compact representation of signals and system behaviors. Importantly, it never sees equations or labels, only raw (input, output)-pairs. By the time it reaches a real motor, the model already understands what the “systems” look like.

Step 2: Infer a Motor from a Single Run

When deployed, the model watches one untuned motor response, exactly the kind of imperfect data you’d get from a default PI controller. From that single example, it forms a system embedding: a learned internal representation that captures the motor’s load dynamics. That embedding is then used as a prompt to generate accurate feedforward control signals for new motion commands.

In testing, the team used an Analog Devices Trinamic motor controller TMC9660 to drive both stepper and brushless DC (BLDC) motors under varied loads for testing. The result: the model generated commands that dramatically reduced tracking error on the very first try. No retraining. No identification experiments. No parameter tweaking.

In motor control, precision isn’t just about hitting numbers on a graph. Rapid, automated tuning and lower tracking error translate into higher reliability, lower energy costs, and shorter downtime thanks to faster motor replacement times.

In experiments, this approach outperformed both well-tuned PI controllers and physics-based feedforward methods across unseen loads and motors. The improvement was especially noticeable for complex multi-inertia systems and motors where nonlinear effects like Coulomb friction become significant. In other words, it doesn’t just work in the lab, it holds up where traditional tuning is hardest.

One of the most counterintuitive insights from this work is the role of synthetic data. The initial synthetic dataset was comprised of 60,000 input–output pairs from a diverse set of 20,000 systems. These systems spanned a diverse array of motor types, including linear and nonlinear time-invariant (LTI and NTI) systems. The team further augmented that dataset with samples of common nonlinearities, such as dead zones and saturation. That helped introduce motor behaviors that are not motor-specific.

What stands out in this data is that model pretraining avoided specific motor data or system equations. Crucially, tests showed that even a model trained only on simple linear systems outperforms both PI and physics-based approaches, meaning the training data doesn’t need to closely match the real motor. The model doesn’t need a perfect digital twin of every motor. It benefits more from diversity than realism, learning a wide range of possible dynamics so it can recognize new ones quickly.

Even when synthetic systems don’t exactly match real motors, the model still generalizes effectively. This flips the usual assumption about data on its head and opens the door to far more scalable control systems.

This approach doesn’t eliminate control theory. It doesn’t replace feedback loops. Instead, it removes the most expensive part of the process: manual tuning and system identification. What once took hours of expert effort can now happen in seconds. This shift holds enormous promise for robotics, manufacturing, medical devices, and consumer products; especially anywhere motors need to work well without constant human intervention.

In practice, these advancements are best realized on integrated motion platforms like TMC9660 and TMC6460, where power electronics, sensing, and motion control are tightly integrated into a single, software-defined solution.