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Dynamic recurrent neural networks for stable adaptive control of wing rock motion.

机译：动态递归神经网络用于机翼岩石运动的稳定自适应控制。

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Wing rock is a self-sustaining limit cycle oscillation (LCO) which occurs as the result of nonlinear coupling between the dynamic response of the aircraft and the unsteady aerodynamic forces. In this thesis, dynamic recurrent RBF (Radial Basis Function) network control methodology is proposed to control the wing rock motion. The concept based on the properties of the Presiach hysteresis model is used in the design of dynamic neural networks. The structure and memory mechanism in the Preisach model is analogous to the parallel connectivity and memory formation in the RBF neural networks. The proposed dynamic recurrent neural network has a feature for adding or pruning the neurons in the hidden layer according to the growth criteria based on the properties of ensemble average memory formation of the Preisach model. The recurrent feature of the RBF network deals with the dynamic nonlinearities and endowed temporal memories of the hysteresis model.; The control of wing rock is a tracking problem, the trajectory starts from non-zero initial conditions and it tends to zero as time goes to infinity. In the proposed neural control structure, the recurrent dynamic RBF network performs identification process in order to approximate the unknown non-linearities of the physical system based on the input-output data obtained from the wing rock phenomenon. The design of the RBF networks together with the network controllers are carried out in discrete time domain. The recurrent RBF networks employ two separate adaptation schemes where the RBF's centre and width are adjusted by the Extended Kalman Filter in order to give a minimum networks size, while the outer networks layer weights are updated using the algorithm derived from Lyapunov stability analysis for the stable closed loop control. The issue of the robustness of the recurrent RBF networks is also addressed. The effectiveness of the proposed dynamic recurrent neural control methodology is demonstrated through simulations to suppress the wing rock motion in AFTI/F-16 testbed aircraft having the delta wing configuration. The potential implementation as well as the practicality of the control methodology are also discussed.
机译：机翼岩石是一种自我维持的极限循环振荡（LCO），是飞机动态响应和不稳定的空气动力之间非线性耦合的结果。本文提出了一种动态递归RBF网络控制方法来控制机翼的岩石运动。基于Presiach磁滞模型特性的概念被用于动态神经网络的设计。 Preisach模型中的结构和内存机制类似于RBF神经网络中的并行连接和内存形成。所提出的动态循环神经网络具有根据Preisach模型的整体平均记忆形成特性，根据生长标准在隐藏层中添加或修剪神经元的功能。 RBF网络的循环特征处理滞后模型的动态非线性和赋予的时间记忆。机翼岩石的控制是一个跟踪问题，其轨迹从非零初始条件开始，并且随着时间趋于无穷大而趋于零。在提出的神经控制结构中，循环动态RBF网络执行识别过程，以便基于从机翼岩石现象获得的输入输出数据来近似物理系统的未知非线性。 RBF网络和网络控制器的设计在离散时域中进行。循环RBF网络采用两种独立的自适应方案，其中RBF的中心和宽度由扩展卡尔曼滤波器进行调整，以提供最小的网络大小，而外部网络层权重则使用从Lyapunov稳定性分析得出的算法进行更新，以实现稳定。闭环控制。还解决了循环RBF网络的鲁棒性问题。通过仿真证明了在具有三角翼配置的AFTI / F-16试验飞机上抑制机翼岩石运动的仿真结果证明了所提出的动态递归神经控制方法的有效性。还讨论了控制方法的潜在实现方式和实用性。

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