Event-driven based online adaptive trajectory planning for the UAV slung-payload transportation system
This paper proposes a new event-driven based online trajectory planning strategy for the nonlinear optimal adjustment control of the payload's swing motion for the quadrotor slung-payload transportation system.By using the approximate structure of the neural network,the optimal control law of the payload's swing angle is obtained by training the neural network under the framework of the event-driven adaptive critic network.At the same time,the flight trajectory of the quadrotor unmanned aerial vehicle(UAV)is further planned based on the optimal control law,which achieves accurate position regulation of the UAV and fast suppression of the payload's swing motion during the flight while the computation cost of the UAV's airborne processor is reduced significantly.Then,it is proved that the output weight estimation error of the improved neural network is uniformly ultimately bounded,and the convergence of the quadrotor's positioning and payload's swing suppression is proved via the Lyapunov based stability analysis.Finally,flight experimental results are presented to validate the effectiveness of the proposed trajectory planning strategy comparing with other methods.
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