modelcontrolled

Modelcontrolled, often written as model-controlled or model-based control, is a term used to describe systems, strategies, or decisions that are governed by an explicit model of the environment or process. In practice, the phrase is used to indicate reliance on a mathematical, data-driven, or empirical model to predict outcomes and determine actions. The term is not uniformly standardized and may be used interchangeably with related concepts such as model-based control or model-driven control.

In engineering and robotics, model-based control uses a model of the plant to compute control actions, estimate states, or forecast future behavior. Common approaches include model predictive control, state observers, and optimal control methods. In reinforcement learning and computational neuroscience, the term model-controlled is often associated with model-based strategies that plan ahead using a model, as opposed to model-free strategies that rely on cached action values.

Key considerations for model-controlled designs include model accuracy, identification, uncertainty, computational load, and robustness. The quality of the model affects stability, performance, and safety, especially under disturbances or unmodeled dynamics.

Applications span industrial process control, autonomous systems, robotics, energy management, and economics, where decisions are guided by an explicit representation of the system dynamics.

Variants and related terms include model-based control, model-driven control, and model-predictive control. The terminology varies across disciplines, so readers should check context to determine intended meaning.