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Event-sampled control of quadrotor unmanned aerial vehicle using neural networks

机译：基于神经网络的四旋翼无人机事件采样控制

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In this paper, an event-sampled output-feedback neural network (NN) controller for a quadrotor unmanned aerial vehicle (UAV) is considered. First an observer design is presented, allowing the need for a full knowledge of the state-vector to be avoided. Next, a kinematic controller is designed in order to find a desired translational velocity; the information provided by the kinematic controller will be used in the design of a virtual controller wherein a desired rotational velocity will be determined such that the UAV's orientation converges to its desired value. Finally, the information from the observer, the kinematic controller, and the virtual controller are used in the design of a dynamic controller where NNs will be implemented to approximate uncertainties in the UAV's dynamics; the signals generated by the dynamic controller will ensure that the desired lift velocity and the desired rotational velocities are tracked. In all these designs, the effects of sampling errors are highlighted. Next, by designing an appropriate event-execution law, the sampling errors are shown to be bounded during the inter-event period. Finally, the effectiveness of the proposed event-sampled controller will be demonstrated with simulation results.
机译：本文考虑了四旋翼无人机（UAV）的事件采样输出反馈神经网络（NN）控制器。首先，提出了一个观察者设计，可以避免对状态向量的全面了解。接下来，设计运动控制器，以便找到所需的平移速度。运动控制器提供的信息将用于虚拟控制器的设计中，其中将确定所需的旋转速度，以使UAV的方向收敛到其所需的值。最后，来自观察者，运动学控制器和虚拟控制器的信息被用于动态控制器的设计中，其中将采用神经网络来近似无人机动态的不确定性。动态控制器生成的信号将确保跟踪所需的提升速度和所需的旋转速度。在所有这些设计中，突出显示了采样误差的影响。接下来，通过设计适当的事件执行定律，表明采样误差在事件间周期内是有界的。最后，将通过仿真结果证明所提出的事件采样控制器的有效性。

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