Ultra-Precise Position Measurement Using Resolvers and AI

Resolvers remain one of the most robust and widely trusted position sensing technologies in military, aerospace, and industrial systems. They are valued for their ability to survive extreme environments, tolerate contamination and vibration, and continue operating reliably for decades. At the same time, modern platforms increasingly demand levels of accuracy, stability, and observability that push against the practical limits of any purely analog sensing system.

Traditionally, improvements in resolver-based measurement systems have come from better analog electronics, better excitation sources, better shielding, and better calibration procedures. These techniques remain important and continue to deliver incremental gains. However, they all operate within the same fundamental model: the resolver is treated as a device that produces a signal which is then measured as accurately as possible.

A different and complementary approach is to treat the resolver not as a perfect sensor to be measured, but as a physical system whose behavior can be modeled, observed, and corrected. In this view, the measured signals are not simply converted into position. They are interpreted in the context of expected behavior, historical data, environmental conditions, and system dynamics. This is where modern estimation techniques and machine learning begin to play a practical role.

Every real resolver system exhibits imperfections. There are amplitude imbalances, phase errors, harmonic distortion, temperature-dependent gain shifts, mechanical eccentricity, and installation-related misalignments. In conventional systems, these errors are handled through a combination of careful calibration and conservative performance margins. The system is designed so that worst-case errors remain acceptable, even if that means leaving performance on the table under most conditions.

With modern processing resources available at the edge and in networked acquisition systems, it becomes possible to take a more nuanced approach. Instead of assuming a static error model, the system can continuously observe its own behavior and adapt its interpretation of the sensor signals over time. This does not require replacing the resolver or changing its fundamental operating principle. It requires changing how the data is interpreted.

In practical terms, this often starts with more sophisticated estimation. Rather than computing angle directly from instantaneous sine and cosine measurements, the system can incorporate temporal behavior, known motion constraints, and consistency checks across time. Even simple model-based observers can significantly reduce noise and suppress transient disturbances without adding latency or compromising stability.

Machine learning techniques extend this idea further by allowing the system to learn complex, non-linear error patterns that are difficult to capture in closed-form analytical models. For example, temperature-dependent distortion, load-dependent mechanical effects, or subtle installation-specific behaviors can be characterized during operation and compensated in software. The resolver and its analog front end become part of a larger, adaptive measurement system rather than a fixed transfer function.

It is important to emphasize that this is not about trusting an opaque algorithm with a safety-critical measurement. In well-designed systems, AI-based techniques operate as a refinement layer on top of a fundamentally sound measurement chain. The baseline measurement remains available, observable, and bounded. The estimation and correction layers improve accuracy and stability under normal conditions while preserving predictable behavior under abnormal ones.

One of the enabling factors for this approach is the increasing availability of processing capability in distributed acquisition systems such as CommandNet Edge and Digital Commander. When high-quality, time-aligned resolver data is available in digital form close to the source, it becomes feasible to apply more advanced signal processing and estimation techniques without imposing unacceptable latency or bandwidth costs.

Another enabling factor is the ability to correlate position data with other measurements. Motor currents, temperatures, vibration sensors, and even structural strain measurements can all provide context about the operating conditions of the mechanism. Sensor fusion techniques allow the position estimator to use this additional information to improve confidence, detect anomalies, or adapt its internal models over time.

In some applications, this can translate directly into measurable performance improvements. Noise can be reduced without increasing filtering delay. Repeatability can be improved even when the underlying mechanics exhibit wear or temperature sensitivity. Calibration intervals can be extended because the system is continuously monitoring and compensating for slow changes in behavior.

There are also diagnostic benefits. A system that models its own measurement process can often detect when the sensor or mechanics are beginning to deviate from normal behavior. Subtle changes in learned correction terms or residual errors can serve as early indicators of developing faults, long before they become obvious in raw position data.

As with any technique applied in safety-critical or mission-critical systems, the engineering discipline lies in how these methods are introduced. The goal is not to replace deterministic measurement with probabilistic inference, but to combine them in a way that preserves trust, traceability, and bounded behavior while extracting more usable information from the same physical sensor.

Resolvers will continue to be used because they solve a hard physical problem in a simple and robust way. What is changing is not the sensor, but the sophistication of the system that interprets its signals. By combining high-quality acquisition hardware with modern estimation, fusion, and adaptive correction techniques, it is possible to push resolver-based position measurement to levels of precision, stability, and insight that were previously difficult to achieve.

In this sense, AI is not replacing traditional engineering. It is becoming another tool in the system engineer’s toolbox, applied carefully and conservatively to extract more value from proven hardware in increasingly demanding applications.