Kontrollmodeller

Kontrollmodeller, or control models, are mathematical representations of a system used to design and analyze controllers that regulate the system’s behavior. They describe how inputs, disturbances and internal states influence outputs, and they form the basis for predicting how a controlled process will respond to control actions. In practice, a control model often sits between the physical system (the plant) and the controller, and may include sensors, actuators and feedback mechanisms.

Models come in various forms. Linear versus nonlinear, deterministic versus stochastic, and continuous-time versus discrete-time representations are common classifications. The most widely used formalisms include state-space models, transfer function models, and sets of differential equations. In many cases, a simplified or linearized version of a nonlinear system is employed to facilitate controller design, with the understanding that nonlinearity or time variance may require adaptation or robustness considerations.

Model development typically follows a pipeline: selecting a model structure, identifying parameters from data (system identification), and validating the model against independent measurements. Once a credible model exists, it supports controller design methods such as classical control (PID with lead/lag elements), robust control (H-infinity), optimal control (LQR), and model predictive control (MPC). Adaptive and learning-based approaches adjust the model or controller as the system changes.

Applications of kontrollmodeller span process industries, manufacturing, energy systems, aviation, automotive, and robotics. Key challenges include nonlinearity, time delays, disturbances, and model mismatch, which affect stability and performance. Evaluation focuses on stability, robustness, transient response, and computational practicality.