We present a new method to design the controller for Mars capsule atmospheric entry using deep neural networks and flight-proven Apollo entry data. The controller is trained to modulate the bank angle with data from the Apollo entry simulations. The neural network controller reproduces the classical Apollo results over a variation of entry state initial conditions. Compared to the Apollo controller as a baseline, the present approach achieves the same level of accuracy for both linear and nonlinear entry dynamics. The Apollo-trained controller is then applied to Mars entry missions. As in Earth environment, the controller achieves the desired level of accuracy for Mars missions using both linear and nonlinear entry dynamics with higher uncertainties in the entry states and the atmospheric density. The deep neural network is only trained with data from Apollo reentry simulation in an Earth model and works in both Earth and Mars environments. It achieves the desired landing accuracy for a Mars capsule. This method works with both linear and nonlinear integration and can generate the bank angle commands in real-time without a prestored trajectory.

中文翻译：

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用于火星大气进入的简单且精确的阿波罗训练神经网络控制器

我们提出了一种使用深层神经网络和经过飞行验证的阿波罗进入数据设计火星胶囊大气进入控制器的新方法。控制器经过培训，可以使用来自Apollo进入模拟的数据来调节坡度。神经网络控制器在进入状态初始条件的变化下重现经典的Apollo结果。与作为基准的Apollo控制器相比，本方法对于线性和非线性进入动力学都达到了相同水平的精度。然后将经过阿波罗训练的控制器应用于火星进入任务。与在地球环境中一样，控制器使用线性和非线性进入动力学以及进入状态和大气密度的较高不确定性，实现了火星飞行任务所需的精确度。深度神经网络仅接受来自地球模型中Apollo再入模拟的数据的训练，并且可以在地球和火星环境中使用。它实现了火星胶囊所需的着陆精度。该方法可用于线性和非线性积分，并且可以实时生成倾斜角命令，而无需预先存储轨迹。