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Related Concept Videos

Control Systems01:10

Control Systems

1.7K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Feedback control systems01:26

Feedback control systems

638
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
638
PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
549
Control Systems: Applications01:25

Control Systems: Applications

1.0K
Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
1.0K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.5K
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...
1.5K
Control System Problem01:21

Control System Problem

339
In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...
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Updated: Dec 28, 2025

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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CPS-Based Self-Adaptive Collaborative Control for Smart Production-Logistics Systems.

Zhengang Guo, Yingfeng Zhang, Xibin Zhao

    IEEE Transactions on Cybernetics
    |February 23, 2020
    PubMed
    Summary
    This summary is machine-generated.

    A novel self-adaptive collaborative control (SCC) mode enhances smart production-logistics systems using cyber-physical systems (CPS) and industrial Internet of Things (IIoT). This approach improves system intelligence, flexibility, and resilience in discrete manufacturing.

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    Area of Science:

    • Industrial Engineering
    • Manufacturing Systems
    • Control Systems

    Background:

    • Discrete manufacturing systems face challenges in production-logistics synchronization due to operational dynamics and uncertainty.
    • Existing systems often lack the intelligence, flexibility, and resilience needed to handle exceptions effectively.

    Purpose of the Study:

    • To propose a self-adaptive collaborative control (SCC) mode for smart production-logistics systems.
    • To enhance the intelligence, flexibility, and resilience of discrete manufacturing operations.
    • To address exceptions in production-logistics synchronization through advanced control strategies.

    Main Methods:

    • Leveraging cyber-physical systems (CPS) and industrial Internet of Things (IIoT) for real-time data collection and processing.
    • Modeling dynamic manufacturing resource behavior using hybrid automata.
    • Implementing three levels of collaborative control granularity (nodal, local, global SCC).
    • Solving collaborative optimization problems with analytical target cascading (ATC).

    Main Results:

    • Validation of the SCC method's applicability and efficiency through a proof-of-concept simulation.
    • Demonstrated reductions in production waiting time, makespan, and energy consumption.
    • Achieved these improvements within reasonable computational time.

    Conclusions:

    • The proposed SCC mode effectively enhances smart production-logistics systems.
    • Enables manufacturers to implement CPS and IIoT for building resilient and flexible manufacturing environments.
    • Offers a viable solution for managing operational uncertainties and exceptions in discrete manufacturing.