Design of Robust Adaptive Inverse Controller for a Hypersonic Aircraft Based on CMAC Neural Network

Abstract:

A new control structure based on dynamic inversion (DI) and neural network technology for a class of nonlinear uncertain system is proposed. DI is an effective nonlinear tracking and decoupling control method. However, the performance of the current DI may significantly degrade when internal unmodeled dynamics and external disturbances exist. In this paper, a Cerebellar Model Articulation Controller (CMAC) neural network is used to improve overall system performance of robust tracking control. The algorithm convergence condition is shown. Based on Lyapunov stability theory, all signals are proved to be uniform convergence. Finally, the flight control system of the hypersonic vehicle is designed based on the proposed method and the simulation results demonstrate the excellent performance and robustness of the controllers.

• Periodical: Applied Mechanics and Materials (Volumes 325-326)
• Pages: 1135-1139
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.325-326.1135
• Online since: June 2013
• Authors: Cong Chao Yao, Xin Min Wang, Xiao Chen Zhang
• Keywords: CMAC Neural Network, Dynamic Inversion, Hypersonic, Nonlinear Control System

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