sensorswithout

Sensorwithout is a term used in sensing, robotics, and control theory to describe approaches that infer state information without relying on dedicated physical sensors. In this usage, sensor information is obtained through sensorless estimation, indirect measurements, system models, and data-driven methods that reconstruct variables such as position, velocity, temperature, or environmental properties.

Techniques under the sensorwithout umbrella include observers (for example, Kalman or Luenberger observers), back-calculation from measurable signals, and machine learning or system-identification methods that map available data to the target state. These methods rely on plausible models of the dynamics and often combine multiple data sources through estimation or fusion.

Sensorwithout is not a standardized term; it appears in a variety of academic papers and industry discussions to describe sensorless or model-based sensing strategies. It is used across domains where adding sensors is costly, intrusive, or prone to failure, such as automotive drive systems, robotics, process control, and aerospace.

Advantages include reduced hardware cost, lower maintenance, and improved robustness in harsh environments. Limitations center on accuracy and reliability, particularly during transients or when the model is imperfect. Computational requirements and the need for robust fault handling are additional considerations.

See also: sensor fusion, state estimation, Kalman filter, observer design, model predictive control.