The aerodynamic model of flexible wing aircraft is highly nonlinear with continuously time-varying dynamics under kinematic constraints. The nonlinearities stem from the aerodynamic forces and continuous deformations in the flexible wing. In spite of the various experimental attempts and theoretical setups that were made to model these dynamics, an accurate formulation was not achieved. The control paradigms of the aircraft are concerned with the electro-mechanical coupling between the pilot and the wing. It is challenging to design a flight controller for such aircraft while complying with these constraints. In this paper, innovative machine learning technique is employed to design a robust online model-free control scheme for flexible wing aircraft. The controller maintains internal asymptotic stability for the aircraft in real-time using selected set of measurements or states in uncertain dynamical environment. It intelligently incorporates the varying dynamics, geometric parameters, and physical constraints of the aircraft into optimal control strategies. The adaptive learning structure employs a policy iteration approach, taking advantage of Bellman optimality principles, to converge to an optimal control solution for the problem. Artificial neural networks are adopted to implement the adaptive learning algorithm in real-time without prior knowledge of the aerodynamic model of the aircraft. The control scheme is generalized and shown to function effectively for different pilot/wing control mechanisms. It also demonstrated its ability to overcome the undesired stability problems caused by coupling the pilot’s dynamics with the flexible wing’s frame of motion.

中文翻译：

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灵活机翼飞机的在线无模型控制器：基于策略迭代的强化学习方法

柔性机翼飞机的空气动力学模型是高度非线性的，在运动学约束下具有连续时变的动力学。非线性来自于气动力和柔性机翼的连续变形。尽管为模拟这些动力学进行了各种实验尝试和理论设置，但仍未获得准确的公式。飞机的控制范式涉及飞行员与机翼之间的机电耦合。在遵守这些约束的同时，设计用于这种飞机的飞行控制器是具有挑战性的。在本文中，创新的机器学习技术被用于设计一种强大的在线灵活机翼无模型在线控制方案。控制器使用不确定的动态环境中选定的一组测量值或状态，实时维护飞机的内部渐近稳定性。它智能地将不断变化的动力学，几何参数和飞机的物理约束纳入最佳控制策略。自适应学习结构采用策略迭代方法，利用Bellman最优性原则，收敛为该问题的最优控制解决方案。采用人工神经网络实时实现自适应学习算法，无需事先了解飞机的空气动力学模型。该控制方案被概括并显示为对不同的飞行员/机翼控制机构有效地起作用。