scholarly journals Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

2021 ◽  
Vol 5 (CSCW2) ◽  
pp. 1-28
Author(s):  
Sean Kross ◽  
Philip Guo
Keyword(s):  
Outer Loop

Performance of Outer-Loop Control for Adaptive Modulation and Coding Based on Mutual Information in OFDM MIMO SDM

2015 ◽  
Vol E98.B (8) ◽  
pp. 1506-1517 ◽  
Author(s):  
Teppei EBIHARA ◽  
Yasuhiro KUGE ◽  
Hidekazu TAOKA ◽  
Nobuhiko MIKI ◽  
Mamoru SAWAHASHI
Keyword(s):  
Mutual Information ◽  
Adaptive Modulation ◽  
Adaptive Modulation And Coding ◽  
Outer Loop ◽  
Loop Control

scholarly journals Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model

2012 ◽  
Vol 140 (8) ◽  
pp. 2628-2646 ◽  
Author(s):  
Shu-Chih Yang ◽  
Eugenia Kalnay ◽  
Brian Hunt
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Linear Models ◽  
Low Frequency ◽  
Lorenz Model ◽  
Outer Loop ◽  
Nonlinear Growth ◽  
Ensemble Mean ◽  
Ensemble Transform Kalman Filter ◽  
The One

Abstract An ensemble Kalman filter (EnKF) is optimal only for linear models because it assumes Gaussian distributions. A new type of outer loop, different from the one used in 3D and 4D variational data assimilation (Var), is proposed for EnKF to improve its ability to handle nonlinear dynamics, especially for long assimilation windows. The idea of the “running in place” (RIP) algorithm is to increase the observation influence by reusing observations when there is strong nonlinear error growth, and thus improve the ensemble mean and perturbations within the local ensemble transform Kalman filter (LETKF) framework. The “quasi-outer-loop” (QOL) algorithm, proposed here as a simplified version of RIP, aims to improve the ensemble mean so that ensemble perturbations are centered at a more accurate state. The performances of LETKF–RIP and LETKF–QOL in the presence of nonlinearities are tested with the three-variable Lorenz model. Results show that RIP and QOL allow LETKF to use longer assimilation windows with significant improvement of the analysis accuracy during periods of high nonlinear growth. For low-frequency observations (every 25 time steps, leading to long assimilation windows), and using the optimal inflation, the standard LETKF RMS error is 0.68, whereas for QOL and RIP the RMS errors are 0.47 and 0.35, respectively. This can be compared to the best 4D-Var analysis error of 0.53, obtained by using both the optimal long assimilation windows (75 time steps) and quasi-static variational analysis.

A Novel Concept for Energy-Optimal, Independent-Phase Control of Brushless Motor Drivers

2021 ◽  
pp. 1-11
Author(s):  
Amin Ghorbanpour ◽  
Hanz Richter
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Robot Motion ◽  
Control Law ◽  
Outer Loop ◽  
Energy Regeneration ◽  
Brushless Motor ◽  
Control Scheme ◽  
Novel Concept ◽  
Constrained Quadratic Optimization

Abstract In this work, a new drive concept for brushless direct current (BLDC) motors is introduced. Energy regeneration is optimally managed with the aim of improving the energy efficiency of robot motion controls. The proposed scheme has three independent regenerative drives interconnected in a wye configuration. An augmented model of the robot, joint mechanisms, and BLDC motors is formed, and then a voltage-based control scheme is developed. The control law is obtained by specifying an outer-loop torque controller followed by minimization of power consumption via online constrained quadratic optimization. An experiment is conducted to assess the performance of the proposed concept against an off-the-shelf driver. It is shown that, in terms of energy regeneration and consumption, the developed driver has better performance. Furthermore, the proposed concept showed a reduction of 15% energy consumption for the conditions of the study.

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

2018 ◽  
Vol 30 (12) ◽  
pp. 3281-3308
Author(s):  
Hong Zhu ◽  
Li-Zhi Liao ◽  
Michael K. Ng
Keyword(s):  
Dimensionality Reduction ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Main Idea ◽  
Real Data ◽  
Learning Performance ◽  
High Dimensional ◽  
Data Sets ◽  
Outer Loop ◽  
Orthogonality Constraints

We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

scholarly journals A Robust Inner and Outer Loop Control Method for Trajectory Tracking of a Quadrotor

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2147 ◽  
Author(s):  
Dunzhu Xia ◽  
Limei Cheng ◽  
Yanhong Yao
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Outer Loop ◽  
Loop Control

Asymptotic Stability of Hierarchical Inner-Outer Loop-Based Flight Controllers

2008 ◽  
Vol 41 (2) ◽  
pp. 1741-1746 ◽  
Author(s):  
Isabelle Fantoni ◽  
Rogelio Lozano ◽  
Farid Kendoul
Keyword(s):  
Asymptotic Stability ◽  
Outer Loop

scholarly journals Fractional Control of a Lightweight Single Link Flexible Robot Robust to Strain Gauge Sensor Disturbances and Payload Changes

Actuators ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 317
Author(s):  
Saddam Gharab ◽  
Selma Benftima ◽  
Vicente Feliu Batlle
Keyword(s):  
Control System ◽  
Strain Gauge ◽  
High Frequency ◽  
Singular Perturbation Theory ◽  
Strain Gauges ◽  
Input State ◽  
Frequency Noise ◽  
Flexible Robot ◽  
Outer Loop ◽  
High Frequency Noise

In this paper, a method to control one degree of freedom lightweight flexible manipulators is investigated. These robots have a single low-frequency and high amplitude vibration mode. They hold actuators with high friction, and sensors which are often strain gauges with offset and high-frequency noise. These problems reduce the motion’s performance and the precision of the robot tip positioning. Moreover, since the carried payload changes in the different tasks, that vibration frequency also changes producing underdamped or even unstable time responses of the closed-loop control system. The actuator friction effect is removed by using a robust two degrees of freedom PID control system which feeds back the actuator position. This is called the inner loop. After, an outer loop is closed that removes the link vibrations and is designed based on the combination of the singular perturbation theory and the input-state linearization technique. A new controller is proposed for this outer loop that: (1) removes the strain gauge offset effects, (2) reduces the risk of saturating the actuator due to the high-frequency noise of strain gauges and (3) achieves high robustness to a change in the payload mass. This last feature prompted us to use a fractional-order PD controller. A procedure for tuning this controller is also proposed. Simulated and experimental results are presented that show that its performance overcomes those of PD controllers, which are the controllers usually employed in the input-state linearization of second-order systems.

scholarly journals Design and Implementation of Dual-Stage Track-Following Control for Hard Disk Drives

2009 ◽  
Author(s):  
Jianbin Nie ◽  
Roberto Horowitz
Keyword(s):  
Hard Disk Drives ◽  
Servo System ◽  
Hard Disk ◽  
Disk Drive ◽  
Disk Drives ◽  
Outer Loop ◽  
Servo Systems ◽  
Design And Implementation ◽  
Integral Action ◽  
Dual Stage

This paper discusses the design and implementation of two track-following controllers for dual-stage hard disk drive servo systems. The first controller is designed by combining an outer loop sensitivity-decoupling (SD) controller with an inner loop disturbance observer (DOB). The second is designed by combining mixed H2/H∞ synthesis techniques with an add-on integral action. The designed controllers were implemented and evaluated on a disk drive with a PZT-actuated suspension-based dual-stage servo system. Position error signal (PES) for the servo system was obtained by measuring the slider displacement with an LDV and injecting a simulated track runout.

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