Stable Neuro-Flight-Controller Using Fully Tuned Radial Basis Function Neural Networks
Stable Neuro-Flight-Controller Using Fully Tuned Radial Basis Function Neural Networks
复制标题
DOI：
10.2514/2.4793
复制
发表时间：
2001-07
影响因子：
2.6
通讯作者：
Yan Li;N. Sundararajan;P. Saratchandran
中科院分区：
文献类型：
--
作者：
Yan Li;N. Sundararajan;P. Saratchandran
已经开发了一种八种控制方案，其中径向基函数网络（RBFN）辅助传统控制器。 RBFN 控制器由可变高斯函数组成，仅使用在线学习来表示飞机系统的局部逆动力学。通过李亚普诺夫综合方法，导出了更新 RBFN 所有参数（包括中心、宽度以及输出层的权重）的调整规则，这扩展了 Gomi 和 Kawato 的策略，其中只有权重是自适应的。 （Gomi, H. 和 Kawato, M.，“使用反馈误差学习的闭环系统的神经网络控制”，神经网络，第 6 卷，第 7 期，1993 年，第 933 ‐946 页）。所提出的调整规则保证了整个系统的收敛性并大大提高了跟踪精度。使用 F8 飞机纵向模型的仿真研究说明了所提出方案的优越性能。仿真研究进一步表明，结果可以扩展到动态RBFN，其中可以添加/修剪隐藏神经元，从而产生更紧凑的网络结构。
A e ight control scheme in which a radial basis function network (RBFN)aids a conventional controller has been developed. The RBFN controller, consisting of variable Gaussian functions, uses only online learning to represent the local inverse dynamics of the aircraft system. With a Lyapunov synthesis approach, a tuning rule for updating all of the parameters of the RBFN (including centers, widths, as well as the weights of the output layer ) is derived, which extends Gomi and Kawato’ s strategy, where only the weights were adaptable. (Gomi, H., and Kawato, M., “ Neural Network Control for a Closed-Loop System Using Feedback-Error Learning,” Neural Networks , Vol. 6, No. 7, 1993, pp. 933 ‐946). The proposed tuning rule guarantees the convergence of the overall system and greatly improves the tracking accuracy. Simulation studies using an F8 aircraft longitudinal model illustrate the superior performance of the proposed scheme. The simulation studies further indicate that the results can be extended to a dynamic RBFN in which the hidden neurons can be added /pruned, thus producing a more compact network structure.