A path planning strategy for a search and coverage mission for a small UAV that maximizes the area covered based on stored energy and maneuverability constraints is presented. The proposed formulation has a high level of autonomy, without requiring an exact choice of optimization parameters, and is appropriate for real-time implementation. The computed trajectory maximizes spatial coverage while closely satisfying terminal constraints on the position of the vehicle and minimizing the time of flight. Comparisons of this formulation to a path planning algorithm based on those with time constraint show equivalent coverage performance but improvement in prediction of overall mission duration and accuracy of the terminal position of the vehicle.

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance.

The modified Picard-Chebyshev method, when run in parallel, is thought to be more accurate and faster than the most efficient sequential numerical integration techniques when applied to orbit propagation problems. Previous experiments have shown that the modified Picard-Chebyshev method can have up to an order of magnitude speedup over the 12th order Runge-Kutta-Nystrom method. For this study, the evaluation of the accuracy and computational time of the modified Picard-Chebyshev method, using the Java Astrodynamics Toolkit (JAT) high-precision force model, is conducted to assess its runtime performance. Simulation results of the modified Picard-Chebyshev method, implemented in MATLAB and the MATLAB Parallel Computing Toolbox, are compared against the most efficient first and second order Ordinary Differential Equation (ODE) solvers. A total of six processors were used to assess the runtime performance of the modified Picard-Chebyshev method. It was found that for all orbit propagation test cases, where the gravity model was simulated to be of higher degree and order (10 additional function calls to JAT using a 70 degree × 70 order Earth Gravity Model to increase computational overhead to 0.142 seconds per force function call), the modified Picard-Chebyshev method was faster, by as much as 100%, than the other ODE solvers which were tested.

Aircraft dynamics and control (ADC) is a core course requirement for an undergraduate program in aeronautical engineering. This article presents an undertaking that reconfigures the instruction of fundamental concepts in ADC by replacing traditional problem-solving approach with a method that relies on a simulation framework comprising of Matlab, USAF DATCOM, and flight simulation software. The typical challenges experienced by students including their inability to visualize complicated, multi-modal aircraft were overcome by the new method thus enhancing the student's learning experience. The implemented approach improved students' motivational belief and its calibration, their use of active learning strategies and actual performance. ß 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:63-71, 2015; View this article online at wileyonlinelibrary.com/journal/ cae;

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