Adaptive Flight Path Control of Airborne Wind Energy Systems

In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system identification algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system identification algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system identification algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system identification algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response.

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Stabilization of Cascaded Systems via L1 Adaptive Controller with Application to a UAV Path Following Problem and Flight Test Results

2007 American Control Conference ◽

10.1109/acc.2007.4283028 ◽

2007 ◽

Cited By ~ 21

Author(s):

Chengyu Cao ◽

Naira Hovakimyan ◽

Vijay V. Patel ◽

Isaac Kaminer ◽

Vladimir Dobrokhodov

Keyword(s):

Path Following ◽

Flight Test ◽

Adaptive Controller ◽

Test Results ◽

L1 Adaptive Controller ◽

Cascaded Systems

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A Digital Twin Environment Designed for the Implementation of Real Time Monitoring Tool

10.1115/power2021-65384 ◽

2021 ◽

Author(s):

Paolo Pezzini ◽

Harry Bonilla ◽

Grant Johnson ◽

Zachary Reinhart ◽

Kenneth Mark Bryden

Keyword(s):

System Identification ◽

Power Plant ◽

Real Time ◽

Data Analytics ◽

Operating Conditions ◽

Identification Algorithm ◽

Online System ◽

Digital Twin ◽

Online System Identification ◽

Identification Tool

Abstract Real time models and digital twin environments represent a new frontier that allow the development of supplemental data analytics of measurable and unmeasurable parameters for a variety of power plant configurations. Performance prediction, monitoring of degradation effects, and a faster recognition of anomalous events during power plant load following operations and/or due to cyber-attacks can be easily detected with the support of digital twin environments. In the research work described in this article, a digital twin environment was developed to capture the dynamics of a micro compressor-turbine system modified for hybrid configuration at the Department of Energy’s National Energy Technology Laboratory (NETL). The innovative approach for the development of the digital twin environment was based on creating a compressor-turbine physics-based model using a stateless methodology generally utilized for microservices architectures. The advantage of using this approach was represented by modeling individual or a group of power plant components on distributed computational resources such as clouds in a lightweight and interchangeable manner. Supplemental data analytics were performed using an online system identification tool developed in previous work and applied to an unmeasurable parameter only available in the digital twin system. This work demonstrated the ability to train a recursive algorithm to predict a supplemental parameter for faster anomaly detection or for replacing the physics-based model during design or monitoring of operational systems. The thermodynamic compressor-turbine net power was the unmeasurable parameter only available in the digital twin model, which was predicted with the online system identification tool. Results showed that the online system identification algorithm predicted the dynamic response of the thermodynamic net power based on a set of experimental data points at nominal operating conditions with an absolute mean percentage error of ∼0.644%.

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Calibration of a Subscale F/A-22 Engine Inlet Empirical Model Using Ground and Flight Test Data

Volume 2: Aircraft Engine; Ceramics; Coal, Biomass and Alternative Fuels; Controls, Diagnostics and Instrumentation; Environmental and Regulatory Affairs ◽

10.1115/gt2006-90050 ◽

2006 ◽

Author(s):

Jeffrey F. Monaco ◽

David S. Kidman ◽

Randall L. Bickford ◽

Donald J. Malloy

Keyword(s):

Parameter Estimation ◽

System Identification ◽

Modeling And Simulation ◽

Real Time ◽

Air Force ◽

Test Data ◽

Model Updating ◽

Flight Test ◽

Simulation Tools ◽

Identification Parameter

The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. This common set of modeling and simulation tools incorporates real-time data validation, system identification, parameter estimation model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. These resultant models could also be used for flight manual development, determining compliance to specifications, or to aid in real-time equipment monitoring. This paper describes the integrated approach to system identification, parameter estimation, and model updating. Implementation of a software package to enable efficient model handoff between test groups and centers is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The model parameters are then calibrated with F/A-22 flight test data. Results show that the modeling algorithm captures the relevant nonlinear physics of the application, and the calibration and updating procedure improves the model match to flight data. A companion paper provides preliminary results from integrating the calibrated total-pressure inlet distortion and recovery models into a real-time equipment health monitoring system to support test operations.

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