Closed-loop Control
Closed-loop Control - Related Videos
01:17
Open and closed-loop control systems
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2024
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. An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
02:19
Triggering a Closed-Loop Stimulation by Neuron Spiking Activity
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2025
This video demonstrates the operation of closed-loop stimulation in a rat with a surgically implanted device to record neural activity. Neuronal signals are first recorded and analyzed. Template waveforms are then uploaded, and parameters are defined to trigger closed-loop stimulation.
11:54
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Cited by 1 •
2021
We present protocols and methods of analyses to build co-adaptive interfaces that stream, parameterize, analyze, and modify human body and heart signals in close-loop. This setup interfaces signals derived from the peripheral and central nervous systems of the person with external sensory inputs to help track biophysical change.
11:53
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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2017
We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Cited by 9 •
2015
In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.
FAQs
The sensor first captures a signal describing the system’s current output, while the controller evaluates that signal against a target or reference state. It then determines whether the input should change, and the actuator delivers the revised stimulation or other intervention. This coordinated sequence allows the system to respond to ongoing biological changes rather than applying an unchanging input.
A reference state gives the controller a basis for judging whether the measured output is appropriate. By comparing current neural or behavioral information with that desired condition, the controller can guide adjustments to the input. This comparison is especially relevant when biological activity changes over time, because the intervention can remain linked to the system’s current state.
Neural activity and behavioral changes are two signals identified as useful feedback sources in neuroscience. Sensors collect these signals so the controller can determine how the nervous system or behavior is responding. Using either type of information allows an intervention to be adjusted according to observed activity instead of relying solely on a preset stimulation pattern.
Feedback links the intervention to the nervous system’s current state. When the measured output indicates that conditions have changed, the controller can modify stimulation or another input accordingly. This state-dependent adjustment may improve precision and reduce unnecessary stimulation, making the approach useful for systems that must respond to dynamic neural or behavioral conditions.
A practical workflow begins by collecting neural activity or behavioral information with sensors. The controller then compares the measured output with a target or reference and selects an adjustment. Finally, an actuator changes stimulation or another input, after which the system measures the resulting output again. Repeating this sequence supports ongoing adaptation to the nervous system.
The framework supports brain-computer interfaces, adaptive deep brain stimulation, and neuroprosthetic systems. In each case, real-time neural activity or behavioral changes can inform how the system modifies its input. These applications use feedback to make the intervention more responsive to current conditions, rather than treating the nervous system as a static target.
Closed-loop systems can connect targeted perturbations with measurements of the nervous system’s changing activity or behavior. Researchers can therefore adjust an intervention as the system responds and examine how neural circuits behave under those conditions. This makes the framework relevant not only for adaptive technologies, but also for investigating responses to targeted neural perturbations.