SetPointModelle

SetPointModelle, or set-point models, are theoretical and mathematical frameworks used to describe how systems regulate a state variable toward a target value known as the set point. They are applied across disciplines such as physiology, neuroscience, psychology, and engineering to explain how organisms and machines maintain stability through feedback control.

The core idea is that a sensor measures the current value of a variable, a comparator checks it against the set point, and an actuator or control mechanism adjusts the system to reduce the difference. This negative feedback loop can be implemented with various control strategies, from simple proportional control to integral or more complex, dynamic rules. In many real-world systems, the set point is not fixed but can change over time in response to context, learning, or allostatic demands.

Set points can be rigid or dynamic. Dynamic or adaptive set points allow regulation to shift with circadian rhythms, developmental stages, or environmental conditions. Some models also incorporate anticipation (feedforward) or memory of past states, leading to more robust regulation in the presence of delays and noise.

Applications of SetPointModelle include physiological regulation of body temperature, blood glucose and insulin levels, and body weight, as well as engineering control systems, robotics, and process control. In biology, discussions often address the rigidity of the defended set point, the role of multiple interacting variables, and the distinction between true set-point control and alternative frameworks such as allostasis or hedonic regulation. Historical roots trace to the concepts of homeostasis, with set points providing a concrete target for regulatory feedback.