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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.
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...

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Related Experiment Video

Updated: Oct 17, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems.

Pradeeban Kathiravelu¹, Parisa Sarikhani¹, Ping Gu¹

¹Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA.

IEEE Access : Practical Innovations, Open Solutions

|October 11, 2021

Summary

This summary is machine-generated.

NEXUS is a new framework for closed-loop neuromodulation systems. It uses Software-Defined Workflows and directed hypergraphs to overcome limitations of traditional systems, enabling flexible and scalable control.

Keywords:

Closed-loop simulations neuromodulation control systems workflow orchestration

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Published on: November 12, 2019

Area of Science:

Computational Neuroscience
Systems Biology
Bioinformatics

Background:

Closed-loop neuromodulation systems require complex, cyclical control flows for regulating physiological processes.
Existing workflow frameworks, typically based on directed acyclic graphs (DAGs), lack native support for the cyclical nature of these systems.
This limitation hinders the development and deployment of advanced neuromodulation control strategies.

Purpose of the Study:

To introduce NEXUS, a novel workflow orchestration framework designed for distributed analytics systems.
To enable the creation and management of interoperable closed-loop workflows, overcoming the limitations of DAG-based systems.
To demonstrate the framework's capability in executing complex scientific workflows, particularly for neuromodulation control.

Main Methods:

NEXUS employs a Software-Defined Workflows approach, separating data and control flows.
Workflows are managed through a centralized orchestrator, allowing dynamic composition of microservices.
The framework represents workflows using directed hypergraphs (DHGs) instead of DAGs, inherently supporting cycles.

Main Results:

NEXUS successfully facilitated the seamless execution of closed-loop neuromodulation control systems.
The framework demonstrated feasibility, flexibility, and scalability in modeling and executing cyclical workflows.
Evaluations confirmed NEXUS's performance advantages over traditional DAG-based approaches for such applications.

Conclusions:

NEXUS provides a robust solution for designing and executing complex, closed-loop scientific workflows.
The framework's DHG representation and Software-Defined Workflows approach overcome critical limitations of existing tools.
NEXUS is well-suited for advanced applications like neuromodulation, offering enhanced control and system integration.