IEEE Trans Neural Netw Learn Syst. 2016 Jan;27(1):18-31. doi: 10.1109/TNNLS.2015.2406812. Epub 2015 Mar 16.
This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.
本文提出了一种新颖的自适应控制器，用于控制具有未知惯性矩阵的自主直升机，以渐近跟踪期望轨迹。为了识别姿态动力学模型中包含的未知惯性矩阵，本文提出了一种新的结构标识符，与以前提出的标识符不同，它分别包含神经网络（NN）机制和鲁棒自适应机制。使用神经网络在线补偿未知空气动力，使用鲁棒自适应机制消除过大的神经网络补偿误差和外部干扰的组合，新的鲁棒神经网络标识符在复杂的飞行环境中表现出更好的识别性能。此外，神经网络机制中包含一个优化算法，以减轻在线计算的负担。通过严格的 Lyapunov 论证，证明了惯性矩阵辨识误差、位置跟踪误差和姿态跟踪误差渐近收敛到原点任意小邻域。提供了仿真和实现结果来评估所提出的控制器的性能。
