Kang Yu, Chen Shaofeng, Wang Xuefeng, Cao Yang
IEEE Trans Neural Netw Learn Syst. 2019 Feb;30(2):524-538. doi: 10.1109/TNNLS.2018.2844173. Epub 2018 Jul 2.
Helicopters are complex high-order and time-varying nonlinear systems, strongly coupling with aerodynamic forces, engine dynamics, and other phenomena. Therefore, it is a great challenge to investigate system identification for dynamic modeling and adaptive control for helicopters. In this paper, we address the system identification problem as dynamic regression and propose to represent the uncertainties and the hidden states in the system dynamic model with a deep convolutional neural network. Particularly, the parameters of the network are directly learned from the real flight data of aerobatic helicopter. Since the deep convolutional model has a good performance for describing the dynamic behavior of the hidden states and uncertainties in the flight process, the proposed identifier manifests strong robustness and high accuracy, even for untrained aerobatic maneuvers. The effectiveness of the proposed method is verified by various experiments with the real-world flight data from the Stanford Autonomous Helicopter Project. Consequently, an adaptive flight control scheme including a deep convolutional identifier and a backstepping-based controller is presented. The stability of the flight control scheme is rigorously proved by the Lyapunov theory. It reveals that the tracking errors for both the position and attitude of unmanned helicopter asymptotic converge to a small neighborhood of the origin.
直升机是复杂的高阶时变非线性系统，与气动力、发动机动力学及其他现象紧密耦合。因此，研究直升机的动态建模系统辨识和自适应控制极具挑战性。在本文中，我们将系统辨识问题作为动态回归来处理，并提出用深度卷积神经网络表示系统动态模型中的不确定性和隐藏状态。特别地，网络参数直接从特技直升机的实际飞行数据中学习。由于深度卷积模型在描述飞行过程中隐藏状态和不确定性的动态行为方面表现良好，所提出的辨识器即使对于未经训练的特技动作也具有很强的鲁棒性和高精度。通过使用来自斯坦福自主直升机项目的实际飞行数据进行各种实验，验证了所提方法的有效性。因此，提出了一种包括深度卷积辨识器和基于反步法的控制器的自适应飞行控制方案。利用李雅普诺夫理论严格证明了飞行控制方案的稳定性。结果表明，无人直升机位置和姿态的跟踪误差渐近收敛到原点的一个小邻域内。
