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• W2021037753 abstract "Motion planning and trajectory control are two basic challenges of unmanned vehicles. In motion planning problem, feasible trajectories are developed while nonlinear dynamic model and performance constraints of the vehicle under utility are considered. In this study, motion planning is performed via an enhanced particle swarm optimization algorithm. The resulting offline generated trajectories are tracked using a nonlinear trajectory control system methodology. The Lyapunov-based constrained backstepping approach and command filters are utilized in designing the trajectory control system. Command filters smoothen the input signals and provide their derivatives. Evaluation of the proposed integrated approach in several simulated scenarios has effectively demonstrated the potential of both algorithms in generating optimal contour matching trajectories as well as excellent tracking capability of the trajectory control system." @default.
• W2021037753 created "2016-06-24" @default.
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• W2021037753 date "2012-01-06" @default.
• W2021037753 modified "2023-09-27" @default.
• W2021037753 title "Integrated motion planning and trajectory control system for unmanned air vehicles" @default.
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• W2021037753 doi "https://doi.org/10.1177/0954410011432244" @default.
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