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首页> 外文会议>International Conference on Control, Automation and Systems >Reinforcement learning based flight controller capable of controlling a quadcopter with four, three and two working motors
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Reinforcement learning based flight controller capable of controlling a quadcopter with four, three and two working motors

机译：基于强化学习的飞行控制器，能够控制四个，三个工作电机的Quadcopter

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In this research, we show how a reinforcement learning based algorithm called Fault-Tolerant Bio-inspired Flight Controller (FT-BFC) is capable of training a single neural network based model to fly a quadcopter with two, three, and four working rotors. Our algorithm can learn a low-level flight controller that directly controls angular velocities of motors to fly a quadcopter when it has four fully functional motors, and also, despite having one or two motor failures (That is, our proposed flight controller is a fault-tolerant controller as well). In the training and running of our controller, we do not use any conventional flight controller, such as a PID or SMC controller. We test our algorithm in a simulation environment, Gazebo simulator, and illustrate our simulation results that backing up our algorithm capabilities. Finally, before concluding our paper, we discuss the implementation of our algorithm in a real quadcopter.
机译：在这项研究中，我们展示了一种称为容错生物启发飞行控制器（FT-BFC）的加强学习算法如何训练基于单个神经网络的模型，以与两个，三个和四个工作转子一起飞行Quadcopter。我们的算法可以学习一个低级飞行控制器，当它有四个全功能电机时，直接控制电机的角速度，并且尽管具有一个或两个电机故障（即我们所提出的飞行控制器是一个故障-Tolerant控制器也是如此。在我们的控制器的培训和运行中，我们不使用任何传统的飞行控制器，例如PID或SMC控制器。我们在仿真环境，凉亭模拟器中测试算法，并说明了备份算法功能的模拟结果。最后，在结束我们的论文之前，我们讨论了我们在实际Quadcopter中的算法的实施。

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