Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function

Authors:
Beibei Ren;Shuzhi Sam Ge;Keng Peng Tee;Tong Heng Lee
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore and Institute of Intelligent Systems and Information Technology, University of Electronic Science and ...;Institute for Infocomm Research, Agency for Science, Research and Technology, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

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 neurocontrol 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.