mudelikontroll

Mudeli kontroll, or model-based control, refers to control strategies that rely on a mathematical model of the controlled process to predict behavior and optimize control actions. The central idea is to use the model to forecast future states and outputs and to choose control signals that achieve a desired objective while respecting constraints.

One of the most widely used approaches in mudeli kontroll is model predictive control (MPC). In MPC, an optimization problem is solved over a moving horizon to determine a sequence of control moves, of which only the first is applied before the problem is re-solved at the next time step. Other important approaches include internal model control (IMC), state-space feedback with observers, adaptive control that adjusts the model online, and robust control that accounts for model uncertainty.

The model can be physics-based, data-driven (identified from measurements), or a hybrid. Model development often involves system identification, validation, and periodic updating to reflect changes in the process. The quality of the model directly affects performance, stability, and constraint satisfaction.

Mudeli kontroll is particularly advantageous for multivariable systems with difficult constraints and required performance specifications. It offers explicit handling of constraints, anticipates future behavior, and can improve efficiency and safety. Limitations include computational demand and sensitivity to model inaccuracies, which are mitigated by robust, adaptive, or hybrid strategies. It is widely applied in process industries, automotive control, robotics, energy systems, and building management.