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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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Publication Search Results

Now showing 1 - 10 of 11

Adaptive, Integrated Guidance and Control Design for Line-of-Sight-Based Formation Flight

(Georgia Institute of Technology, 2007-10) 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. We formulate the problem as an adaptive output feedback control problem for a line-of-sight-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 6 degrees-of-freedom simulation model are presented to illustrate the efficacy of the approach by comparing the performance with an adaptive timescale separation-based guidance and control design.
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.
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.
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.
An Adaptive Vision-Based Approach to Decentralized Formation Control

(Georgia Institute of Technology, 2004-12) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Evers, Johnny H.

In considering the problem of formation control in the deployment of intelligent munitions, it would be highly desirable, both from a mission and a cost perspective, to limit the information that is transmitted between vehicles in formation. We have proposed an adaptive output feedback approach to address this problem. Adaptive formation controllers are designed that allow each vehicle in formation to maintain separation and relative orientation with respect to neighboring vehicles, while avoiding obstacles. We have implemented two approaches for formation control, namely, leader-follower formations and leaderless formations. In leader-follower formations, there is a unique leader and all the other vehicles are followers. In leaderless formations, there is no unique leader. Each vehicle tracks line-of-sight range to up to two nearest vehicles while simultaneously navigating towards a common set of waypoints. As our results show, such leaderless formations can perform maneuvers like splitting to go around obstacles, rejoining after negotiating the obstacles, and changing into line-shaped formation in order to move through narrow corridors.
An Adaptive Vision-based Approach to Decentralized Formation Control

(Georgia Institute of Technology, 2004-08) Sattigeri, Ramachandra J. ; Calise, Anthony J. ; Evers, Johnny H.

In considering the problem of formation control in the deployment of intelligent munitions, it would be highly desirable, both from a mission and a cost perspective, to limit the information that is transmitted between vehicles in formation. In a previous paper, we proposed an adaptive output feedback approach to address this problem. Adaptive formation controllers were designed that allow each vehicle in formation to maintain separation and relative orientation with respect to neighboring vehicles, while avoiding obstacles. In this paper, we consider a modification to the adaptive control law that enables each vehicle in a leader-follower formation to track line-of-sight (LOS) range with respect to two or more neighboring vehicles with zero steady-state error. We also propose a coordination scheme in which each vehicle tracks LOS range to up to two nearest vehicles while simultaneously navigating towards a common set of waypoints. This coordination scheme does not require a unique leader for the formation, increasing robustness of the formation. As our results show, such leaderless formations can perform maneuvers like splitting to go around obstacles, rejoining after negotiating the obstacles, and changing into line-shaped formation in order to move through narrow corridors.