Virtual Reference Feedback Tuning for Cascade Control Systems

2016 ◽  
Vol 28 (5) ◽  
pp. 739-744 ◽  
Author(s):  
Huy Quang Nguyen ◽  
◽  
Osamu Kaneko ◽  
Yoshihiko Kitazaki ◽  
Keyword(s):  
Control Systems ◽  
Cascade Control ◽  
Data Driven ◽  
Experiment Data ◽  
Feedback Controllers ◽  
Virtual Reference ◽  
Text Data ◽  
Tuning Method ◽  
Data Driven Approach ◽  
The Cost

[abstFig src='/00280005/17.jpg' width='300' text='Data-driven approach to cascade control systems' ] Virtual Reference Feedback Tuning (VRFT), proposed by Campi et al., is an effective data-driven tuning method used in feedback controllers because the desired parameters implemented in the controller are obtained by using only one-shot experiment data. In this paper, we apply VRFT to cascade control systems. We also discuss the meaning of the cost function to be minimized. A numerical example is demonstrated to show an effectiveness and validity of our proposed method.

scholarly journals Reducing the Energy Budget in WSN Using Time Series Models

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Felicia Engmann ◽  
Ferdinand Apietu Katsriku ◽  
Jamal-Deen Abdulai ◽  
Kofi Sarpong Adu-Manu
Keyword(s):  
Data Transmission ◽  
Energy Savings ◽  
Data Driven ◽  
Wireless Sensor ◽  
Time Series Models ◽  
Duty Cycling ◽  
Data Driven Approach ◽  
Save Energy ◽  
The Cost ◽  
Prediction Approach

Energy conservation is critical in the design of wireless sensor networks since it determines its lifetime. Reducing the frequency of transmission is one way of reducing the cost, but it must not tamper with the reliability of the data received at the sink. In this paper, duty cycling and data-driven approaches have been used together to influence the prediction approach used in reducing data transmission. While duty cycling ensures nodes that are inactive for longer periods to save energy, the data-driven approach ensures features of the data that are used in predicting the data that the network needs during such inactive periods. Using the grey series model, a modified rolling GM(1,1) is proposed to improve the prediction accuracy of the model. Simulations suggest a 150% energy savings while not compromising on the reliability of the data received.

scholarly journals Data-driven estimation of the sensitivity of target-oriented time-lapse seismic imaging to source geometry

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. R47-R58 ◽  
Author(s):  
Andrey H. Shabelansky ◽  
Alison Malcolm ◽  
Michael Fehler
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Time Lapse ◽  
Data Driven ◽  
Target Region ◽  
Data Driven Approach ◽  
Cost Of Time ◽  
The Cost ◽  
Time Lapse Imaging ◽  
Source Geometry

The goal of time-lapse imaging is to identify and characterize regions in which the earth’s material properties have changed between surveys. This requires an effective deployment of sources and receivers to monitor the region where changes are anticipated. Because each source adds to the acquisition cost, we should ensure that only those sources that best image the target are collected and used to form an image of the target region. This study presents a data-driven approach that estimates the sensitivity of target-oriented imaging to source geometry. The approach is based on the propagation of the recorded baseline seismic data backward in time through the entire medium and coupling it with the estimated perturbation in the subsurface. We test this approach using synthetic surface seismic and time-lapse VSP field-data from the SACROC field. These tests show that the use of the baseline seismic data enhances the robustness of the sensitivity estimate to errors, and can be used to select data that best image a target zone, thus increasing the signal-to-noise ratio of the image of the target region and reducing the cost of time-lapse acquisition, processing, and imaging.

scholarly journals A Data-Driven Approach to Identify Flight Test Data Suitable to Design Angle of Attack Synthetic Sensor for Flight Control Systems

Aerospace ◽  
2020 ◽  
Vol 7 (5) ◽  
pp. 63 ◽  
Author(s):  
Angelo Lerro ◽  
Alberto Brandl ◽  
Manuela Battipede ◽  
Piero Gili
Keyword(s):  
Control Systems ◽  
Test Data ◽  
Flight Control ◽  
Flight Test ◽  
Data Driven ◽  
Training Set ◽  
Consequent Reduction ◽  
Data Driven Approach ◽  
Data Points ◽  
Data Pruning

Digital avionic solutions enable advanced flight control systems to be available also on smaller aircraft. One of the safety-critical segments is the air data system. Innovative architectures allow the use of synthetic sensors that can introduce significant technological and safety advances. The application to aerodynamic angles seems the most promising towards certified applications. In this area, the best procedures concerning the design of synthetic sensors are still an open question within the field. An example is given by the MIDAS project funded in the frame of Clean Sky 2. This paper proposes two data-driven methods that allow to improve performance over the entire flight envelope with particular attention to steady state flight conditions. The training set obtained is considerably undersized with consequent reduction of computational costs. These methods are validated with a real case and they will be used as part of the MIDAS life cycle. The first method, called Data-Driven Identification and Generation of Quasi-Steady States (DIGS), is based on the (i) identification of the lift curve of the aircraft; (ii) augmentation of the training set with artificial flight data points. DIGS’s main aim is to reduce the issue of unbalanced training set. The second method, called Similar Flight Test Data Pruning (SFDP), deals with data reduction based on the isolation of quasi-unique points. Results give an evidence of the validity of the methods for the MIDAS project that can be easily adopted for generic synthetic sensor design for flight control system applications.

scholarly journals Learning-Based Model Predictive Control: Toward Safe Learning in Control

2020 ◽  
Vol 3 (1) ◽  
pp. 269-296 ◽  
Author(s):  
Lukas Hewing ◽  
Kim P. Wabersich ◽  
Marcel Menner ◽  
Melanie N. Zeilinger
Keyword(s):  
Model Predictive Control ◽  
Control Systems ◽  
Predictive Control ◽  
Closed Loop ◽  
Data Driven ◽  
Control Techniques ◽  
Safety Constraints ◽  
Recorded Data ◽  
The Cost ◽  
Significant Opportunity

Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.

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