The controller parameters are typically matched to the process characteristics and since the process may change, it is important that the controller parameters are chosen in such a way that the closed loop system is not sensitive to variations in process dynamics. One way to characterize sensitivity is through the nominal sensitivity peak : where and denote the plant and controller's transfer function in a basic closed loop control system written in the Laplace domain using unity negative feedback. The sensitivity function , which appears in the above formula also describes the transfer function from external disturbance to process output. In fact, assuming an additive disturbance n after the output of the plant, the transfer functions of the closed loop system are given by Hence, lower values of suggest further attenuation of the external disturbance. The sensitivity function tells us how the disturbances are influenced by feedback. Disturbances with frequencies such that is less than one are reduced by an amount equal to the distance to the critical point and disturbances with frequencies such that is larger than one are amplified by the feedback. It is important that the largest value of the sensitivity function be limited for a control system and it is common to require that the maximum value of the sensitivity function, , be in a range of 1.3 to 2. (Wikipedia).
Sensitivity (Electrical Engineering)
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The Step Response | Control Systems in Practice
Check out the other videos in this series: https://www.youtube.com/playlist?list=PLn8PRpmsu08pFBqgd_6Bi7msgkWFKL33b This video covers a few interesting things about the step response. We’ll look at what a step response is and some of the ways it can be used to specify design requirements f
From playlist Control Systems in Practice
Chapter 12 Sensitivity Specificity Predictive Values Odds Ratios
Ever wandered how to calculate sensitivity, specificity, positive and negative predictive values or odds ratios or even simply what these terms mean? Watch this short lecture.
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Optimizing a Flight Control System
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Optimize the parameters of a flight control system to simultaneously meet time-domain and frequency-domain design requirements. For more videos, visit http://www.mathworks.
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A control system has two main goals: get the system to track a setpoint, and reject disturbances. Feedback control is pretty powerful for this, but this video shows how feedforward control can make achieving those goals easier. Temperature Control in a Heat Exchange Example: http://bit.ly
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Control Bootcamp: Sensitivity and Complementary Sensitivity (Part 2)
Here we explore the sensitivity and complementary sensitivity functions, which are critical in understanding robustness and performance. (Part 2) Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning con
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Jenne Driggers - Improving the sensitivity of gravitational wave interferometers - IPAM at UCLA
Recorded 01 December 2021. Jenne Driggers of the California Institute of Technology presents "Improving the sensitivity of gravitational wave interferometers" at IPAM's Workshop IV: Big Data in Multi-Messenger Astrophysics. Abstract: The LIGO interferometers collect enormous amounts of dat
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Marcello Delitala: Combination therapies and drug resistance in heterogeneous tumoral populations
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This video describes some of the fundamental limitations of robustness, including time delays and right-half plane zeros. Code available at: faculty.washington.edu/sbrunton/control_bootcamp_code.zip These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunto
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The Gang of Six in Control Theory | Control Systems in Practice
Check out the other videos in the series: Part 1 - What Does a Control Engineer Do? https://youtu.be/ApMz1-MK9IQ Part 2 - What Is Gain Scheduling? https://youtu.be/YiUjAV1bhKs Part 3 - What Is Feedforward Control? https://youtu.be/FW_ay7K4jPE Part 4 - Why Time Delay Matters https://youtu.b
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Gabriele Vajente - Machine Learning and Gravitational Wave Detectors - IPAM at UCLA
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Magnetic resonance at the quantum limit and beyond by Patrice Bertet
Open Quantum Systems DATE: 17 July 2017 to 04 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore There have been major recent breakthroughs, both experimental and theoretical, in the field of Open Quantum Systems. The aim of this program is to bring together leaders in the Open Q
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These lectures are from the EE102, the Stanford course on signals and systems, taught by Stephen Boyd in the spring quarter of 1999. More information is available at https://web.stanford.edu/~boyd/ee102/
From playlist EE102: Introduction to Signals & Systems
EE102: Introduction to Signals & Systems, Lecture 21
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