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Related Concept Videos

Control Systems01:10

Control Systems

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...
Feedback control systems01:26

Feedback control systems

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

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 and...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires careful...

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Related Experiment Video

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A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Information space receding horizon control.

Z Sunberg, S Chakravorty, R Scott Erwin

    IEEE Transactions on Cybernetics
    |June 13, 2013
    PubMed
    Summary

    This study introduces a receding horizon method to solve complex sensor scheduling problems. This approach simplifies computationally difficult dynamic programming problems for optimal sensor management.

    Area of Science:

    • Optimization Theory
    • Control Systems Engineering
    • Information Theory

    Background:

    • Optimal sensor scheduling is often modeled as a partially observed Markov decision problem.
    • Solving these problems typically requires computationally intractable information space (I-space) dynamic programming (DP).

    Discussion:

    • A novel simulation-based stochastic optimization technique is presented.
    • This technique, combined with a receding horizon strategy, bypasses the need for solving high-dimensional I-space DP problems.
    • The method is applied to a sensor scheduling scenario involving N dynamical systems.

    Key Insights:

    • The receding horizon approach effectively addresses the computational complexity of optimal sensor scheduling.
    • The proposed technique maximizes information gain regarding an aggregate system over an infinite horizon.

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  • This method is particularly suited for high-dimensional dynamic programming problems inherent in sensor management.

    • Outlook:

    • Further research can explore extensions of this receding horizon technique to more complex sensor networks.
    • Investigating adaptive or learning-based variations of the optimization method could enhance real-time performance.
    • The approach has potential applications in various fields requiring efficient data acquisition and system monitoring.