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Aerospace Design Group

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https://hdl.handle.net/1853/70743

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1134

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Publication Search Results

Now showing 1 - 10 of 15

An LMI-based Stability Analysis for Adaptive Controllers

(Georgia Institute of Technology, 2009-06) Yang, Bong-Jun ; Yucelen, Tansel ; Calise, Anthony J. ; Shin, Jong-Yeob

We develop a Linear Matrix Inequality (LMI) tool for analyzing the stability and performance of adaptive controllers that employ σ−modification. The formulation involves recasting the error dynamics composed of the tracking error and the weight estimator error into a linear parameter varying form. We show how stability, convergence rate, domain of attraction, and the transient and steady state behavior of the adaptive control system can be analyzed using the developed LMI tool. It is guaranteed that less conservative estimates for the convergence rate and the size of the ultimate bound for the tracking error are obtained compared to the standard analysis in the literature.
Vision-based Target Tracking with Adaptive Target State Estimator

(Georgia Institute of Technology, 2007-08) Sattigeri, Ramachandra J. ; Johnson, Eric N. ; Calise, Anthony J. ; Ha, Jin-Cheol

This paper presents an approach to vision-based target tracking with a neural network (NN) augmented Kalman filter as the adaptive target state estimator. The vision sensor onboard the follower (tracker) aircraft is a single camera. Real-time image processing implemented in the onboard flight computer is used to derive measurements of relative bearing (azimuth and elevation angles) and the maximum angle subtended by the target aircraft on the image plane. These measurements are used to update the NN augmented Kalman filter. This filter generates estimates of the target aircraft position, velocity and acceleration in inertial 3D space that are used in the guidance and flight control law to guide the follower aircraft relative to the target aircraft. Applications of the presented approach include vision-based autonomous formation flight, pursuit and autonomous aerial refueling. The NN augmenting the Kalman filter estimates the target acceleration and hence provides for robust state estimation in the presence of unmodeled target maneuvers. Vision-in-the-loop simulation results obtained in a 6DOF real-time simulation of vision-based autonomous formation flight are presented to illustrate the efficacy of the adaptive target state estimator design.
Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems

(Georgia Institute of Technology, 2007-08) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Kim, Byoung Soo

This paper presents a method to integrate adaptive estimation and adaptive control designs for a class of uncertain nonlinear systems having both parametric uncertainties and unmodeled dynamics. The method is based on Lyapunov-like stability analysis of all the errors in the closed-loop system. The adaptive estimator considered is a linear, time-varying Kalman filter augmented by the output of an observer neural network. The observer neural network compensates the nominal Kalman filter for modeling errors. The estimated states are used in the construction of an adaptive control solution that is based on approximate feedback linearization augmented with the outputs of an adaptive neural network controller. The presented approach is then applied to a vision-based formation flight control problem. The objective is for a follower aircraft to maintain range from a maneuvering leader aircraft using a monocular fixed camera for passive sensing of the leader's relative motion. In the implementation, the states of the adaptive estimator are estimates of line-of-sight variables and the outputs of the observer neural network are estimates of the leader acceleration. The adaptive control solution considered is an integrated guidance and control design that includes online adaptation to unmodeled nonlinearities such as the unknown leader aircraft acceleration and parametric uncertainties in the own-aircraft aerodynamic derivatives. Simulation results using a nonlinear 6DOF simulation model of a fixed-wing UAV are presented to illustrate the feasibility and efficacy of the approach.
Adaptive Control of Evolving Gossamer Structures

(Georgia Institute of Technology, 2006-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.

A solar sail is an example of a gossamer structure that is proposed as an propulsion system for future space missions. Since it is a large scale flexible structure that requires a long time for its deployment, active control may be required to prevent it from deviating into a non-recoverable state. In this paper, we conceptually address control of an evolving flexible structure using a growing double pendulum model. Controlling an evolving system poses a major challenge to control design because it involves time-varying parameters, such as inertia and stiffness. By employing a neural network based adaptive control, we illustrate that the evolving double pendulum can be effectively regulated when fixed-gain controllers are deficient due to presence of time-varying parameters.
Neural Network Augmented Kalman Filtering in the Presence of Unknown System Inputs

(Georgia Institute of Technology, 2006-08) Sattigeri, Ramachandra J. ; Calise, Anthony J.

We present an approach for augmenting a linear, time-varying Kalman filter with an adaptive neural network (NN) for the state estimation of systems with linear process models acted upon by unknown inputs. The application is to the problem of tracking maneuvering targets. The unknown system inputs represent the effect of unmodeled disturbances acting on the system and are assumed to be continuous and bounded. The NN is trained online to estimate the unknown inputs. The training signal for the NN consists of two error signals. The first error signal is the residual of the Kalman filter that is augmented with the NN output. The second error signal is obtained after deriving a linear parameterization model of available system signals in terms of the ideal, unknown NN weights that linearly parameterize the unknown system inputs. The combination of two different sources of error signals to train the NN represents a composite adaptation type approach to adaptive state estimation. The approach is applied in a vision-based formation flight simulation of a leader and a follower unmanned aerial vehicle (UAV). The adaptive estimator onboard the follower UAV estimates the range, azimuth angle, and elevation angle to the leader UAV, the derivatives of these LOS variables, and the unknown leader aircraft acceleration along the axes of the Cartesian coordinate inertial frame. Simulation results with the presented approach are greatly improved when compared to those obtained with just a linear, time-varying Kalman filter and a particular adaptive state estimation method that utilizes just one source of error signals to train the NN [17].
Adaptive, Integrated Guidance and Control Design for Line-of-Sight based Formation Flight

(Georgia Institute of Technology, 2006-08) Kim, Byoung Soo ; Calise, Anthony J. ; Sattigeri, Ramachandra J.

This paper presents an integrated guidance and control design for formation flight using a combination of adaptive output feedback and backstepping techniques without an underlying time-scale separation assumption. We formulate the problem as an adaptive output feedback control problem for a line-of-sight (LOS) based formation flight configuration of a leader and a follower aircraft. The design objective is to regulate range and two bearing angle rates while maintaining turn coordination. Adaptive neural networks are trained online with available measurements to compensate for unmodeled nonlinearities in the design process. These include uncertainties due to unknown leader aircraft acceleration, and the modeling error due to parametric uncertainties in the aircraft aerodynamic derivatives. One benefit of this approach is that the guidance and flight control design process is integrated. Simulation results using a nonlinear 6DOF simulation model are presented to illustrate the efficacy of the approach by comparing the performance with a time-scale separation based design.
6-DOF Nonlinear Simulation of Vision-based Formation Flight

(Georgia Institute of Technology, 2005-08) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Kim, Byoung Soo ; Volyanskyy, Konstantin ; Kim, Nakwan

This paper presents an adaptive guidance and control law algorithm for implementation on a pair of Unmanned Aerial Vehicles (UAVs) in a 6 DOF leader-follower formation flight simulation. The objective of the simulation study is to prepare for a flight test involving a pair of UAVs in formation flight where the follower aircraft will be equipped with an onboard camera to estimate the relative distance and orientation to the leader aircraft. The follower guidance law is an adaptive acceleration based guidance law designed for the purpose of tracking a maneuvering leader aircraft. We also discuss the limitations of a preceding version of the guidance algorithm shown in a previous paper. Finally, we discuss the design of an adaptive controller (autopilot) to track the commands from the guidance algorithm. Simulation results for different leader maneuvers are presented and analyzed.
Adaptive Control for a Microgravity Vibration Isolation System

(Georgia Institute of Technology, 2005-08) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I. ; Whorton, Mark S.

Most active vibration isolation systems that try to a provide quiescent acceleration environment for space-science experiments have utilized linear design methods. In this paper, we address adaptive control augmentation of an existing classical controller that combines a high-gain acceleration inner-loop feedback together with a low-gain position outer-loop feedback to regulate the platform about its center position. The control design considers both parametric and dynamic uncertainties because the isolation system must accommodate a variety of payloads having different inertial and dynamic characteristics. An important aspect of the design is the accelerometer bias. Two neural networks are incorporated to adaptively compensate for the uncertainties within the acceleration and the position loop. A novel feature in the design is that high-band pass and low pass filters are applied to the error signal used to adapt the weights in the neural network and the adaptive signals, so that the adaptive processes operate over targeted ranges of frequency. This prevents the inner and outer loop adaptive processes from interfering with each other. Simulations show that adaptive augmentation improves the performance of the existing acceleration controller and at the same time reduces the maximal position deviation and thus also improves the position controller.
Estimation and Guidance Strategies for Vision-based Target Tracking

(Georgia Institute of Technology, 2005-06) Calise, Anthony J. ; Johnson, Eric N. ; Sattigeri, Ramachandra J. ; Watanabe, Yoko ; Madyastha, Venkatesh

This paper discusses estimation and guidance strategies for vision-based target tracking. Specific applications include formation control of multiple unmanned aerial vehicles (UAVs) and air-to-air refueling. We assume that no information is communicated between the aircraft, and only passive 2-D vision information is available to maintain formation. To improve the robustness of the estimation process with respect to unknown target aircraft acceleration, the nonlinear estimator (EKF) is augmented with an adaptive neural network (NN). The guidance strategy involves augmenting the inverting solution of nonlinear line-of-sight (LOS) range kinematics with the output of an adaptive NN to compensate for target aircraft LOS velocity. Simulation results are presented that illustrate the various approaches.
Approaches to Vision-Based Formation Control

(Georgia Institute of Technology, 2004-12) Johnson, Eric N. ; Calise, Anthony J. ; Sattigeri, Ramachandra J. ; Watanabe, Yoko ; Madyastha, Venkatesh

This paper implements several methods for performing vision-based formation flight control of multiple aircraft in the presence of obstacles. No information is communicated between aircraft, and only passive 2-D vision information is available to maintain formation. The methods for formation control rely either on estimating the range from 2-D vision information by using Extended Kalman Filters or directly regulating the size of the image subtended by a leader aircraft on the image plane. When the image size is not a reliable measurement, especially at large ranges, we consider the use of bearing-only information. In this case, observability with respect to the relative distance between vehicles is accomplished by the design of a time-dependent formation geometry. To improve the robustness of the estimation process with respect to unknown leader aircraft acceleration, we augment the EKF with an adaptive neural network. 2-D and 3-D simulation results are presented that illustrate the various approaches.