tilstandsnærming

Tilstandsnærming is a concept in control theory and dynamical systems that refers to the process of estimating the internal state of a system when the full state is not directly measurable. Many physical systems, such as robots, chemical reactors, or aircraft, have multiple internal variables that determine their behavior, but only a limited number of these variables can be directly observed through sensors. Tilstandsnærming aims to reconstruct the unmeasured state variables using the available measurements and a mathematical model of the system's dynamics.

The core idea behind tilstandsnærming is to combine predictions from a system model with actual measurements. A tilstandsnærmer, also known as a state observer or state estimator, uses the system's equations of motion to predict how the state will evolve over time. When new measurements become available, these are compared to the predicted outputs. The difference, or error, is then used to correct the predicted state, providing a more accurate estimate of the true state.

Common methods for tilstandsnærming include the Luenberger observer, which is a deterministic approach, and the Kalman filter, which is a stochastic approach that optimally estimates the state in the presence of noise. The choice of method depends on the characteristics of the system, the quality of the measurements, and the desired accuracy of the state estimate. Tilstandsnærming is crucial for many control applications, as it allows for the design of feedback controllers that can act on the unmeasurable internal states of a system, leading to improved performance and stability.