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Open and closed-loop control systems

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Effects of feedback

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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Control Systems

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Positive and Negative Feedback Loops

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Feedback control systems

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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
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Self-organized critical noise amplification in human closed loop control.

Felix Patzelt¹, Markus Riegel, Udo Ernst

¹Institute for Theoretical Physics, University of Bremen Germany.

Frontiers in Computational Neuroscience

|October 24, 2008

Summary

Human motor control exhibits unusual fluctuations. This study suggests these arise from adaptive control systems with limited memory, a finding supported by a new model and human balancing experiments.

Keywords:

adaptation fluctuations learning multiplicative noise non-gaussianity power law self-organized criticality sensory-motor system

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Area of Science:

Neuroscience
Control Theory
Human Motor Control

Background:

Human closed-loop control tasks, such as standing or balancing, show non-Gaussian fluctuations with long-tailed distributions.
The underlying cause of these fluctuations in human behavior remains unknown.

Purpose of the Study:

Investigate if self-organized critical noise amplification, arising from adaptive controllers with finite memory stabilizing unstable dynamics, causes these non-Gaussian fluctuations.
Formulate and test a realistic model of adaptive closed-loop control incorporating memory constraints and delays.

Main Methods:

Developed a theoretical model of adaptive closed-loop control with memory and delay constraints.
Conducted psychophysical experiments where human participants balanced an unstable target on a screen.
Compared model predictions with experimental data on human control dynamics.

Main Results:

The model successfully reproduced the long-tailed distributions observed in human behavior.
The model also replicated other characteristic features of human control dynamics.
Fine-tuning the model allowed for the identification of subject-specific control system parameters.

Conclusions:

Self-organized critical noise amplification in adaptive control systems with finite memory may explain non-Gaussian fluctuations in human motor control.
The human nervous system appears to estimate system parameters online with high efficiency, using short observation periods.