Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function.
In this brief, adaptive neural control is presented for a class of output feedback nonlinear systems in the presence of unknown functions. The unknown functions are handled via on-line neural network (NN) control using only output measurements. A barrier Lyapunov function (BLF) is introduced to address two open and challenging problems in the neuro-control area: 1) for any initial compact set, how to determine a priori the compact superset, on which NN approximation is valid; and 2) how to ensure that the arguments of the unknown functions remain within the specified compact superset. By ensuring boundedness of the BLF, we actively constrain the argument of the unknown functions to remain within a compact superset such that the NN approximation conditions hold. The semiglobal boundedness of all closed-loop signals is ensured, and the tracking error converges to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
Ren
Beibei
B
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576. helenren.ac@gmail.com
Ge
Shuzhi Sam
SS
Tee
Keng Peng
KP
Lee
Tong Heng
TH
D000222:Adaptation, Physiological / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D016477:Artifacts; D001185:Artificial Intelligence; D005246:Feedback; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted