Title :
Adaptive neural networks control for a class of nonlinear uncertain systems
Author :
Hu, Yancai ; Li, Tieshan ; Li, Junfang ; Li, Qiang
Abstract :
In this paper, an adaptive dynamic surface control scheme is proposed for a class of nonlinear uncertain systems. By using RBF (radial basis function) neural networks to approximate the uncertainties of systems, the problem of singularity is avoided and the trouble caused by "explosion of complexity" in traditional backstepping methods is removed by taking advantage of DSC (dynamic surface control) technique. In addition, the input saturation constrains are taken into consideration in the control design. Finally, this scheme guarantees that the closed-loop system is uniformly ultimately bounded and the tracking error converges to a small neighborhood around zero. The simulations on aircraft are given to demonstrate the effectiveness of the proposed scheme.
Keywords :
adaptive control; aerospace simulation; approximation theory; closed loop systems; control system synthesis; convergence of numerical methods; neurocontrollers; nonlinear control systems; radial basis function networks; uncertain systems; DSC technique; RBF neural networks; adaptive dynamic surface control scheme; adaptive neural network control; aircraft simulation; closed loop system; complexity explosion; control design; convergence; input saturation constrains; nonlinear uncertain system; radial basis function neural networks; tracking error; uncertainty approximation; uniformly ultimately bounded system; Adaptive systems; Aircraft; Atmospheric modeling; Control systems; Mathematical model; Neural networks; Uncertain systems;
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-2144-1
DOI :
10.1109/ICICIP.2012.6391452
