This work provides flight-path geometry, guidance laws, and synchronous camera angles to observe a ground target from an unmanned aerial vehicle. The observation of the target is affected by wind, aircraft performance, and camera limits. Analytic expressions are derived for paths that result in constant line-of-sight orientation of the target relative to the aircraft body frame. Using minimal heuristics, a guidance law based on "good helmsman" behavior is developed and implemented, and stability of its integration with aircraft dynamics is assessed. An observer estimates wind data, which are used to orient path geometry about the target. Results are demonstrated in high-fidelity simulation.= body-fixed axes system x N , y E , z D = navigation axes system x, y, z = position coordinateŝ x = observer state vector y s = cross-track error α = angle of attack β = angle of sideslip ζ = bearing angle of target from the aircraft κ = camera pan angle κ(s) = curvature of desired path at position s λ = camera tilt angle ρ = radius of curvature φ = bank angle χ = course χ s = desired course on orbit χ w = wind direction ("from" convention) ψ = heading ψ p = bearing angle of aircraft from the target ("clock angle" relative to target) ψ w = wind vector orientation (ψ w = χ w + π ) Subscripts and Supersripts ac = aircraft center of gravity b = body-fixed reference frame . Member AIAA. c = command e = Earth reference frame icpt = Intercept m = measured s = Serret-Frenet reference frame tgt = target w = wind ∧ = estimate
This work is an extension of previous research at Georgia Tech. It demonstrates the fault-tolerance capabilities of a non-linear adaptive controller architecture.The XV-15 tiltrotor in landing configuration is used for demonstration. The failures considered affect longitudinal and lateral channels. Augmentation is provided successfully in all channels.Linear inversion at nominal operating point,
In the application of adaptive flight control, significant issues arise due to vehicle input characteristics such as actuator position limits, actuator position rate limits, and linear input dynamics. The concept of modifying a reference model to prevent an adaptation law from "seeing" and adapting-to these system characteristics is introduced. A specific adaptive control method based on this concept, termed Pseudo-Control Hedging, is introduced that accomplishes this for any Model Reference Adaptive Controller that includes approximate feedback linearization. This method enables continued adaptation while the plant input is saturated. Acceptance and flight certification of an online Neural Network adaptive control law for the X-33 Reusable Launch Vehicle technology demonstrator is discussed as motivation for this work. Simulation results applying the method to the X-33 are described.
This report compares several different methodologies for tracking a moving target with multiple Unmanned Aerial Vehicles (UAVs). Relative position coordination of UAVs is enforced. The comparison considers minimization of heuristics and robustness of performance and stability when the UAVs are exposed to wind and target motion.
This work provides algorithms for flight path guidance and synchronous camera angles to observe a target. The observation of the target is affected by the environment, maximum aircraft performance, and camera limits. Analytic expressions are derived for trajectories required for constant line-of-sight orientation relative to the aircraft. A guidance law based on 'good helmsman' behavior is implemented. An observer is used to estimate wind data, which is used to orient the trajectories about the target. Results are demonstrated in simulated maneuvers with exposure to variable wind and turbulence.
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