A Time Domain Data-Driven Approach for the Estimation of Closed-Loop Stability Margin

2018 ◽  
Author(s):  
Hao Luo ◽  
Tianyu Liu ◽  
Shen Yin
Keyword(s):  
Time Domain ◽  
Closed Loop ◽  
Stability Margin ◽  
Data Driven ◽  
Time Domain Data ◽  
Data Driven Approach ◽  
Loop Stability

Application of Mixed H2/H∞ Data Driven Control Design to Dual Stage Hard Disk Drives

2018 ◽  
Author(s):  
Omid Bagherieh ◽  
Prateek Shah ◽  
Roberto Horowitz
Keyword(s):  
Closed Loop ◽  
Transfer Functions ◽  
Hard Disk Drives ◽  
Control Design ◽  
Hard Disk ◽  
Data Driven ◽  
Disk Drives ◽  
Design Strategies ◽  
Data Driven Approach ◽  
Dual Stage

A data driven control design approach in the frequency domain is used to design track following feedback controllers for dual-stage hard disk drives using multiple data measurements. The advantage of the data driven approach over model based approach is that, in the former approach the controllers are directly designed from frequency responses of the plant, hence avoiding any model mismatch. The feedback controller is considered to have a Sensitivity Decoupling Structure. The data driven approach utilizes H∞ and H2 norms as the control objectives. The H∞ norm is used to shape the closed loop transfer functions and ensure closed loop stability. The H2 norm is used to constrain and/or minimize the variance of the relevant signals in time domain. The control objectives are posed as a locally convex optimization problem. Two design strategies for the dual-stage hard disk drive are presented.

scholarly journals Amplifying the Neural Power Spectrum

2019 ◽  
Author(s):  
J. Andrew Doyle ◽  
Paule-Joanne Toussaint ◽  
Alan C. Evans
Keyword(s):  
Time Domain ◽  
Random Noise ◽  
Power Spectra ◽  
Data Driven ◽  
Background Signal ◽  
Data Driven Approach ◽  
Novel Method ◽  
Human Faces ◽  
First Time ◽  
Scientific Hypotheses

AbstractWe introduce a novel method that employs a parametric model of human electroen-cephalographic (EEG) brain signal power spectra to evaluate cognitive science experiments and test scientific hypotheses. We develop the Neural Power Amplifier (NPA), a data-driven approach to EEG pre-processing that can replace current filtering strategies with a principled method based on combining filters with log-arithmic and Gaussian magnitude responses. Presenting the first time domain evidence to validate an increasingly popular model for neural power spectra [1], we show that filtering out the 1/f background signal and selecting peaks improves a time-domain decoding experiment for visual stimulus of human faces versus random noise.

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