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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 37

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.
Design and Implementation of a Self-configuring Ad-hoc Network for Unmanned Aerial Systems

(Georgia Institute of Technology, 2007-10) Christmann, Hans Claus ; Johnson, Eric N.

Unmanned aerial vehicles (UAVs), and unmanned aerial systems (UAS) as such in general, need wireless networks in order to communicate. UAS are very flexible and hence allow for a wide range of missions by means of utilizing different UAVs according to the mission requirements. Each of these missions also poses special needs and requirements on the communication network. Especially, mission scenarios calling for UAV swarms increase the complexity and call for specialized communication solutions. This work focuses on these specialties and needs and describes the selection process, adaptation and implementation of an ad-hoc routing protocol tailored to an UAV surrounding and a correspondingly adapted communication method.
Adaptive Neural Network Flight Control Using both Current and Recorded Data

(Georgia Institute of Technology, 2007-08) Chowdhary, Girish ; Johnson, Eric N.

Modern aerospace vehicles are expected to perform beyond their conventional flight envelopes and exhibit the robustness and adaptability to operate in uncertain environments. Augmenting proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. The current adaptive methodologies which use Neural Network based control methods use only the instantaneous states to tune the adaptive gains. This results in a rank one limitation on the adaptive law. In this paper we propose a novel approach to adaptive control, which uses the current or the online information as well as stored or background information for adaptation. We show that using a combined online and background learning approach it is possible to overcome the rank one limitation on the adaptive law resulting in faster adaptation to the unknown dynamics. Furthermore, we show that using combined online and background learning methods it is possible to guarantee long term learning in the adaptive flight controller, which enhances performance of the controller when it encounters a maneuver that has been performed in the past. We use Lyapunov based methods for showing boundedness of all signals for a proposed method. The performance of the proposed method is evaluated in the high fidelity simulation environment for the GTMAX UAS maintained by the Georgia Tech UAV lab. The simulation results show that the proposed method exhibits long term learning and faster adaptation leading to better performance of the UAS flight controller.
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 Output Feedback Control of a Flexible Base Manipulator

(Georgia Institute of Technology, 2007-07) Yang, Bong-Jun ; Calise, Anthony J. ; Craig, James I.

This paper considers augmentation of an existing inertial damping mechanism by neural network-based adaptive control, for controlling a micromanipulator that is serially attached to a macromanipulator. The approach is demonstrated using an experimental test bed in which the micromanipulator is mounted at the tip of a cantilevered beam that resembles a macromanipulator with its joint locked. The inertial damping control combines acceleration feedback with position control for the micromanipulator so as to simultaneously suppress vibrations caused by the flexible beam while achieving precise tip positioning. Neural network-based adaptive elements are employed to augment the inertial damping controller when the existing control system becomes deficient due to modeling errors and uncertain operating conditions. There were several design challenges that had to be faced from an adaptive control perspective. One challenge was the presence of a nonminimum phase zero in an output feedback adaptive control design setting in which the regulated output variable has zero relative degree. Other challenges included flexibility in the actuation devices, lack of control degrees of freedom, and high dimensionality of the system dynamics. In this paper we describe how we overcame these difficulties by modifying a previous augmenting adaptive approach to make it suitable for this application. Experimental results are provided to illustrate the effectiveness of the augmenting approach to adaptive output feedback control design.
Real-Time Vision-Based Relative Aircraft Navigation

(Georgia Institute of Technology, 2007-03) Johnson, Eric N. ; Calise, Anthony J. ; Watanabe, Yoko ; Ha, Jin-Cheol ; Neidhoefer, James C.

This paper describes two vision-based techniques for the navigation of an aircraft relative to an airborne target using only information from a single camera fixed to the aircraft. These techniques are motivated by problems such as "see and avoid", pursuit, formation flying, and in-air refueling. By applying an Extended Kalman Filter for relative state estimation, both the velocity and position of the aircraft relative to the target can be estimated. While relative states such as bearing can be estimated fairly easily, estimating the range to the target is more difficult because it requires achieving valid depth perception with a single camera. The two techniques presented here offer distinct solutions to this problem. The first technique, Center Only Relative State Estimation, uses optimal control to generate an optimal (sinusoidal) trajectory to a desired location relative to the target that results in accurate range-to-target estimates while making minimal demands on the image processing system.The second technique, Subtended Angle Relative State Estimation, uses more rigorous image processing to arrive at a valid range estimate without requiring the aircraft to follow a prescribed path. Simulation results indicate that both methods yield range estimates of comparable accuracy while placing different demands on the aircraft and its systems.
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.
Flight Results of Autonomous Fixed-Wing UAV Transitions to and from Stationary Hover

(Georgia Institute of Technology, 2006-08) Johnson, Eric N. ; Turbe, Michael A. ; Wu, Allen D. ; Kannan, Suresh K. ; Neidhoefer, James C.

Fixed-wing unmanned aerial vehicles (UAVs) with the ability to hover have significant potential for applications in urban or other constrained environments where the combination of fast speed, endurance, and stable hovering flight can provide strategic advantages. This paper discusses the use of dynamic inversion with neural network adaptation to provide an adaptive controller capable of transitioning a fixed-wing UAV to and from hovering flight in a nearly stationary position. This approach allows utilization of the entire low speed flight envelope even beyond stall conditions. The method is applied to the GTEdge, an 8.75 foot wing span fixed-wing aerobatic UAV which has been fully instrumented for autonomous flight. Results from actual flight test experiments of the system where the airplane transitions from high speed steady flight into a stationary hover and then back are presented.
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].