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Controller Configurations

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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.
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Related Experiment Video

Updated: Jul 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Performance study of Kalman filter controller for multiconjugate adaptive optics.

Piotr Piatrou¹, Michael C Roggemann

¹Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, Michigan 49931-1200, USA.

|March 6, 2007

Summary

The Kalman filter (KF) algorithm outperforms the minimum variance (MV) control for adaptive optics, especially with low sampling rates. Adding temporal prediction to MV control improves its performance significantly.

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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

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Published on: April 4, 2017

Bringing the Visible Universe into Focus with Robo-AO

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

Related Experiment Videos

Last Updated: Jul 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Bringing the Visible Universe into Focus with Robo-AO

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

Area of Science:

Astronomy
Optical Engineering
Control Systems

Background:

Adaptive optics (AO) systems are crucial for high-resolution astronomical imaging.
System latencies and atmospheric turbulence introduce significant errors in AO performance.
Advanced control algorithms are needed to mitigate these errors effectively.

Purpose of the Study:

To compare the performance of Kalman filter (KF)-based and minimum variance (MV) control algorithms for zonal adaptive optics.
To evaluate the impact of temporal prediction on MV control performance.
To assess controller capabilities for the Gemini-South 8 m telescope multiconjugate adaptive optics (MCAO) system.

Main Methods:

Implemented and compared KF and MV control algorithms with and without a phase temporal prediction step.
Utilized a first-order autoregressive evolution model for atmospheric turbulence in the KF approach.
Simulated performance on the Gemini-South 8 m telescope MCAO system.

Main Results:

The KF algorithm demonstrated superior turbulence compensation compared to the MV algorithm, particularly at low sampling rates and high latencies.
The MV algorithm with temporal prediction showed performance comparable to the KF algorithm under moderate control latencies.
KF explicitly models atmospheric turbulence temporal behavior, contributing to its enhanced performance.

Conclusions:

KF-based control offers significant advantages for adaptive optics systems facing low sampling rates and large latencies.
Temporal prediction is a viable strategy to enhance MV control performance, approaching KF levels in moderate latency scenarios.
The choice of control algorithm depends on system requirements, computational resources, and latency characteristics.