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Related Concept Videos

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...

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Related Experiment Video

Updated: Jul 6, 2026

Bringing the Visible Universe into Focus with Robo-AO
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Published on: February 12, 2013

Microscale Adaptive Optics: Wave-Front Control with a mu-Mirror Array and a VLSI Stochastic Gradient Descent

T Weyrauch, M A Vorontsov, T G Bifano

    Applied Optics
    |March 25, 2008
    PubMed
    Summary
    This summary is machine-generated.

    This study analyzes adaptive systems using microelectromechanical mirror (mu-mirror) arrays and VLSI control systems. Researchers achieved a high adaptation rate near 6000 iterations/s, enhanced by a secondary feedback loop.

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    Published on: April 1, 2020

    Area of Science:

    • Micro- and Nanotechnology
    • Control Systems Engineering
    • Optoelectronics

    Background:

    • Adaptive systems require efficient control for microscale on-chip elements.
    • Microelectromechanical mirror (mu-mirror) arrays offer potential for dynamic optical adjustments.
    • VLSI (Very-Large-Scale Integration) systems are crucial for high-speed control.

    Purpose of the Study:

    • To analyze the performance of adaptive systems integrating mu-mirror arrays and VLSI control.
    • To evaluate the effectiveness of stochastic parallel perturbative gradient descent for beam-quality optimization.
    • To determine the achievable adaptation rates in such microelectronic systems.

    Main Methods:

    • Utilized 5x5 and 6x6 mu-mirror arrays.
    • Employed a control system with two mixed-mode VLSI chips.
    • Implemented model-free beam-quality metric optimization using stochastic parallel perturbative gradient descent.
    • Incorporated a secondary learning feedback loop to control system parameters.

    Main Results:

    • Achieved an adaptation rate of approximately 6000 iterations per second.
    • Demonstrated successful beam-quality optimization using the specified technique.
    • The secondary feedback loop further increased the system's adaptation rate.

    Conclusions:

    • The integrated system of mu-mirror arrays and VLSI control demonstrates high-performance adaptation.
    • Stochastic parallel perturbative gradient descent is an effective method for model-free optimization in these systems.
    • The adaptive system shows promise for applications requiring rapid optical adjustments.