title: Robust and adaptive backstepping control for nonlinear systems using RBF neural networks.
abstract: In this paper, two different backstepping neural network (NN) control approaches are presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By a special design scheme, the controller singularity problem is avoided perfectly in both approaches. Furthermore, the closed loop signals are guaranteed to be semiglobally uniformly ultimately bounded and the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The control performances of the closed-loop systems can be shaped as desired by suitably choosing the design parameters. Simulation results obtained demonstrate the effectiveness of the approaches proposed. The differences observed between the inputs of the two controllers are analyzed briefly.
authors.affiliation: Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China. lyahui2002@yahoo.com.cn
authors.forename: Yahui
authors.initials: Y
authors.lastname: Li
authors.forename: Sheng
authors.initials: S
authors.lastname: Qiang
authors.forename: Xianyi
authors.initials: X
authors.lastname: Zhuang
authors.forename: Okyay
authors.initials: O
authors.lastname: Kaynak
mesh_terms: D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics