Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

Lawrence, Dale; Frew, Eric W.; Pisano, William J.

doi:10.2514/1.34896

Cited by 217 publications

(179 citation statements)

References 8 publications

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“…The main idea is to design a potential vector field, whose integral lines converge to the desired path. In particular, the general description of the vector field for the path following tasks has been presented in the work (Lawrence et al, 2008); however, even for the simplest model of nonholonomic robots, a comprehensive analysis of this guidance algorithm has been carried out only for special trajectories, such as straight-line and circular paths (Nelson et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…There are many possible approaches to solving this problem: the guidance methods using the Serret-Frenet representation at a projective point (Samson, 1992), virtual target (Soetanto et al, 2003), line-of-sight guidance (Fossen and Pettersen, 2014;Caharija et al, 2015) and differentialgeometric methods for invariant sets stabilization (Akhtar et al, 2015). Compared to these approaches, the methods based on the tracking of vector fields (Nelson et al, 2007;Lawrence et al, 2008) demonstrate better, in many aspects, performance (Caharija et al, 2015;Sujit et al, 2014). The vector field algorithms are widely used in many applications of robotics, such as path-planning (Pamosoaji and Hong, 2013), obstacle avoidance (Hoy et al, 2015) and extremum seeking (Matveev et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Guiding vector field algorithm for a moving path following problem

et al. 2017

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This paper presents a guidance algorithm solving the problem of moving path following, that is, steering a mobile robot to a curvilinear path attached to a moving frame. The nonholonomic robot is described by the unicycle-type model under the influence of some constant exogenous disturbance. The desired path may be an arbitrary smooth curve in its implicit form, that is, a level set of some known smooth function. The path following algorithm employs a guiding vector field, whose integral curves converge to the trajectory. Experiments with a real fixed wing unmanned aerial vehicle (UAV) as well as numerical simulations are presented, illustrating the performance of the proposed path following control algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Guiding vector field algorithm for a moving path following problem

et al. 2017

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“…Many guidance and control techniques have been investigated by researchers: vector field approach [4][5][6][7][8][9][10], line of sight algorithms [11], Serret-Frenet representation based methods [6,12], feedback linearization [13][14][15] and thebackstepping approach [16][17][18]. This paper explores the implementation of a nonlinear guidance and control design technique for a UAV to track a target in 3D environment.…”

Section: Introductionmentioning

confidence: 99%

Target Tracking in 3-D Using Estimation Based Nonlinear Control Laws for UAVs

Ahmed

Subbarao

2016

Aerospace

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This paper presents an estimation based backstepping like control law design for an Unmanned Aerial Vehicle (UAV) to track a moving target in 3-D space. A ground-based sensor or an onboard seeker antenna provides range, azimuth angle, and elevation angle measurements to a chaser UAV that implements an extended Kalman filter (EKF) to estimate the full state of the target. A nonlinear controller then utilizes this estimated target state and the chaser's state to provide speed, flight path, and course/heading angle commands to the chaser UAV. Tracking performance with respect to measurement uncertainty is evaluated for three cases: (1) stationary white noise; (2) stationary colored noise and (3) non-stationary (range correlated) white noise. Furthermore, in an effort to improve tracking performance, the measurement model is made more realistic by taking into consideration range-dependent uncertainties in the measurements, i.e., as the chaser closes in on the target, measurement uncertainties are reduced in the EKF, thus providing the UAV with more accurate control commands. Simulation results for these cases are shown to illustrate target state estimation and trajectory tracking performance.

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“…[1][2][3] Lyapunov vector field guidance was constructed for multiple UAVs to perform a coordinated standoff tracking mission.…”

Section: Introductionmentioning

confidence: 99%

Turning Direction Determination using Error Dynamics-Based Guidance Law for Stationary Target Observation

Kim

2014

Trans. Japan Soc. Aero. S Sci.

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Guidance laws are proposed to enable unmanned aerial vehicles (UAVs) to accomplish stationary target observation missions. In this paper, a nonlinear guidance law is designed utilizing the characteristics of distance error dynamics. The turning direction of the UAVs is very important for multiple UAVs operation. The turning direction of UAV is related to the line-of-sight angular acceleration of the UAV. The proposed guidance law is analyzed to provide a guideline for selecting guidance gains taking into consideration the turning direction. Numerical simulations are performed to verify the performance of the proposed guidance law.

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Lyapunov Vector Fields for Autonomous Unmanned Aircraft Flight Control

Cited by 217 publications

References 8 publications

Guiding vector field algorithm for a moving path following problem

Guiding vector field algorithm for a moving path following problem

Target Tracking in 3-D Using Estimation Based Nonlinear Control Laws for UAVs

Turning Direction Determination using Error Dynamics-Based Guidance Law for Stationary Target Observation

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