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

Kim, Byoung Soo; Calise, Anthony J.; Sattigeri, Ramachandra

doi:10.2514/1.27758

Cited by 28 publications

(12 citation statements)

References 16 publications

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“…where the bounds on with the normal form given by (3), let the adaptive estimator be given by (12), the adaptive control law by (30), (31), the observer NN adaptive law by (25), and the controller NN adaptive law by (44). Then the closed-loop system errors ζ are uniformly ultimately bounded.…”

Section: Lyapunov-like Stability Analysismentioning

confidence: 99%

See 1 more Smart Citation

Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems

Sattigeri

Calise

Kim

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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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.2 presented in Ref. [6][7][8][9] for full relative degree systems. The approach presented in this paper differs from that in Ref. [3,[5][6][7][8][9] in two significant aspects. The approaches in Ref. [3,[5][6][7][8][9] all require knowledge of the dimension of the complete state vector of the system. The approach developed in this paper is applicable to systems with unmodeled dynamics, and does not require knowledge of the dimension of the complete state vector. An adaptive observer is built to estimate only the modeled states of the system. In addition, the approaches in Ref. [3,[5][6][7][8][9] permit augmentation only of a linear time-invariant observer with a NN. In this paper, we consider augmentation of a linear, time-varying observer with a NN. These extensions in theory are important to allow application in certain guidance and flight control applications. For example in applications such as missile intercept guidance, target tracking and vision-based formation flight, not all the states are available for feedback and the target dynamics are the poorly modeled or unmodeled.The problem of leader-follower formation flight in which the follower aircraft is equipped with only an onboard camera to track the leader aircraft is quite challenging. This problem requires simultaneous sensor data processing, state estimation and tracking control in the presence of unmodeled disturbances (leader acceleration) and measurement uncertainties. Sensor data pr...

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Section: Lyapunov-like Stability Analysismentioning

confidence: 99%

“…The adaptive control solution is based on the adaptive IGC design presented in Ref. [12]. Finally, simulation results using a nonlinear 6DOF simulation model of a fixed-wing UAV are presented.…”

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confidence: 99%

Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems

Sattigeri

Calise

Kim

2007

AIAA Guidance, Navigation and Control Conference and Exhibit

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“…Kim et al presented an integrated guidance and control design for formation flight using a combination of adaptive controllers. 11 This paper assumed uncertainties resulting from unknown leader aircraft's acceleration and modeling error due to parametric uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss

Kim

Suk

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper investigates experimental evaluation via flight tests for applying adaptive neural network controller to a flying-wing type unmanned aerial vehicle experiencing partial wing-loss. For this, six-degree-of-freedom numerical model is constructed taking into account damage-induced changes to the unmanned aerial vehicle in aerodynamic coefficients, mass, center of gravity, and moments of inertia. Numerical simulations are performed to investigate the flight dynamics change and to verify the performance of the neural network based controller. During the flight test, main wing-loss is artificially generated by 22% or 33% area moment. The flight test verifies that the damaged unmanned aerial vehicle shows drastic roll behavior with the unstable longitudinal response, and the neural network based adaptive controller combined with feedback linearization successfully compensates for the wing damage.

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“…However, in Sattigeri et al, 15 a three-dimensional formation flight simulation is proposed in which an adaptive acceleration-based guidance law is designed for the purpose of tracking a maneuvering leader aircraft. Moreover, in Kim et al, 16 a combination of adaptive output feedback and backstepping techniques is used for a line of sight-based formation flight configuration of a leader and a follower aircraft. In Ryoo et al, 17 optimal guidance laws for multiple vehicles formation flight are addressed and a method to compensate the acceleration or maneuver of the formation leader is included in the guidance laws.…”

Section: Introductionmentioning

confidence: 99%

Stability of cooperative unmanned aerial vehicles based on relative measurements

Dehghani

Menhaj

2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper describes a guidance method which is applicable in the leader-follower formation control. By considering limitations on communication among the aircraft, using airborne seekers for relative states measurement is proposed, and accordingly a kinematic model for the leader-follower system is developed. A main contribution of this work is analysis of the formation keeping in various flight scenarios when a cascade loop-based guidance strategy is used. In the proposed control structure, to regulate the relative ranges and the relative angles toward the desired values, two separate control channels are designed and to overcome the leader maneuvers, two proper acceleration estimators are employed. The effectiveness of the proposed guidance strategy is evaluated via simulation examples.

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

Cited by 28 publications

References 16 publications

Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems

Integration of Adaptive Estimation and Adaptive Control Design for Uncertain Nonlinear Systems

Flight test of flying-wing type unmanned aerial vehicle with partial wing-loss

Stability of cooperative unmanned aerial vehicles based on relative measurements

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