Title :
Adaptive neural network control for aircraft landing under actuator failures
Author :
Jianjun Ma ; Peng Li ; Lina Geng ; Zhiqiang Zheng
Author_Institution :
Coll. of Mechatron. & Autom., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
Neural network (NN) based adaptive control is investigated for aircraft landing in the present of actuator failures in this work. Radial basis function neural network (RBFNN) in combination with backstepping technique is utilized to develop the virtual control for the overactuated aircraft with dynamic uncertainties and unknown disturbances. By utilizing Lyapunov synthesis, the closed-loop control signals are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin. Control allocation (or reallocation once actuator failures have been detected and isolated) is designed to distribute the virtual control among the redundant actuators with amplitude and rate constraints. Simulation results are presented to show the effectiveness of the proposed control.
Keywords :
Lyapunov methods; actuators; adaptive control; aircraft control; control system synthesis; neurocontrollers; radial basis function networks; stability; Lyapunov synthesis; NN based adaptive control; RBFNN; actuator failures; adaptive neural network control; aircraft landing; amplitude constraints; backstepping technique; closed-loop control signals; control allocation; control reallocation; radial basis function neural network; rate constraints; redundant actuators; semi-globally uniformly ultimately bounded control; virtual control; Actuators; Aerospace control; Aircraft; Artificial neural networks; Atmospheric modeling; Resource management; Neural network; adaptive control; backstepping; control allocation;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
