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Control Systems01:10

Control Systems

1.9K
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...
1.9K
Feedback control systems01:26

Feedback control systems

746
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...
746
Control Systems: Applications01:25

Control Systems: Applications

1.3K
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...
1.3K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.8K
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...
1.8K
Control System Problem01:21

Control System Problem

461
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...
461
Controller Configurations01:22

Controller Configurations

408
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
408

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Related Experiment Video

Updated: Feb 27, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Optimal Triggering of Networked Control Systems.

Ali Heydari

    IEEE Transactions on Neural Networks and Learning Systems
    |July 6, 2017
    PubMed
    Summary

    This study optimizes bandwidth allocation for nonlinear networked control systems using approximate dynamic programming. Algorithms are developed for optimal sensor data transmission scheduling, even with packet loss or unknown system models.

    Area of Science:

    • Control Systems Engineering
    • Networked Control Systems
    • Optimization Theory

    Background:

    • Networked control systems (NCS) face challenges in bandwidth allocation due to data transmission constraints.
    • Efficient scheduling of sensor measurements is crucial for maintaining system performance and stability in NCS.

    Purpose of the Study:

    • To develop an optimal bandwidth allocation strategy for nonlinear NCS.
    • To address challenges including packet dropouts and unknown system dynamics.
    • To ensure system stability and performance through efficient data transmission scheduling.

    Main Methods:

    • Development of an approximate dynamic programming (ADP) algorithm for optimal triggering/scheduling.
    • Extension of the ADP algorithm from fixed-final-time to infinite-horizon problems.

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  • Investigation of Zero-Order-Hold (ZOH) and generalized ZOH scenarios.
  • Establishment of a model-free scheme for learning optimal solutions with unknown models.
  • Main Results:

    • An ADP-based algorithm for optimal bandwidth allocation in nonlinear NCS.
    • Demonstrated effectiveness for various scenarios, including packet dropouts and generalized ZOH.
    • A model-free approach enabling learning of optimal control policies without prior system knowledge.
    • Analysis of convergence, optimality, and stability of the proposed algorithms.

    Conclusions:

    • The developed ADP algorithms provide effective solutions for bandwidth allocation in nonlinear NCS.
    • The model-free scheme offers a robust approach for systems with uncertain dynamics.
    • Numerical analyses confirm the practical potential and performance of the proposed methods.