scholarly journals Bending angle prediction and control of soft pneumatic actuators with embedded flex sensors – A data-driven approach

Mechatronics ◽  
2018 ◽  
Vol 50 ◽  
pp. 234-247 ◽  
Author(s):  
Khaled Elgeneidy ◽  
Niels Lohse ◽  
Michael Jackson
Keyword(s):  
Data Driven ◽  
Bending Angle ◽  
Pneumatic Actuators ◽  
Data Driven Approach ◽  
Prediction And Control ◽  
And Control ◽  
Flex Sensors

scholarly journals Data-Driven Bending Angle Prediction of Soft Pneumatic Actuators with Embedded Flex Sensors

2016 ◽  
Vol 49 (21) ◽  
pp. 513-520 ◽  
Author(s):  
Khaled Elgeneidy ◽  
Niels Lohse ◽  
Michael Jackson
Keyword(s):  
Data Driven ◽  
Bending Angle ◽  
Pneumatic Actuators ◽  
Flex Sensors

scholarly journals Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 187
Author(s):  
Balázs Németh ◽  
Dániel Fényes ◽  
Zsuzsanna Bede ◽  
Péter Gáspár
Keyword(s):  
Traffic Flow ◽  
Optimization Problem ◽  
Control Design ◽  
Prediction Method ◽  
Data Driven ◽  
Traffic Network ◽  
Control Methods ◽  
Prediction And Control ◽  
Data Driven Modeling ◽  
And Control

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Anthony J. Chirico ◽  
Jason R. Kolodziej
Keyword(s):  
Feature Extraction ◽  
Failure Modes ◽  
Extraction Process ◽  
Data Driven ◽  
Ball Screw ◽  
Fault Classification ◽  
Electromechanical Actuators ◽  
Rotating Machine ◽  
Data Driven Approach ◽  
And Control

This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

scholarly journals In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5009 ◽  
Author(s):  
Stefania Tronci ◽  
Paul Van Neer ◽  
Erwin Giling ◽  
Uilke Stelwagen ◽  
Daniele Piras ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Continuous Process ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Monitoring And Control ◽  
Soft Sensors ◽  
Continuous Processes ◽  
Data Driven Approach ◽  
And Control

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

scholarly journals Data-driven approach for monitoring, protection, and control of distribution system assets using micro-PMU technology

2017 ◽  
Vol 2017 (1) ◽  
pp. 1011-1014 ◽  
Author(s):  
Emma Stewart ◽  
Michael Stadler ◽  
Ciaran Roberts ◽  
Jim Reilly ◽  
Dan Arnold ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Driven ◽  
Data Driven Approach ◽  
Protection And Control ◽  
And Control

scholarly journals Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

2020 ◽  
Vol 9 (6) ◽  
pp. 351 ◽  
Author(s):  
Zhihuan Wang ◽  
Mengyuan Yao ◽  
Chenguang Meng ◽  
Christophe Claramunt
Keyword(s):  
Spatial Clustering ◽  
Infection Risk ◽  
Data Driven ◽  
Automatic Identification ◽  
Quick Response ◽  
Support Mechanism ◽  
Risk Levels ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

Sign in / Sign up

Export Citation Format

Share Document