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 addr...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2010-08, Vol.21 (8), p.1339-1345
Main Authors: Ren, Beibei, Ge, Shuzhi Sam, Tee, Keng Peng, Lee, Tong Heng
Format: Article
Language:English
Subjects:
Adaptation, Physiological - physiology
Adaptive control
Adaptive control systems
Algorithms
Animals
Applied sciences
Approximation
Artificial Intelligence
Barrier function
Computer science
control theory
systems
Computer simulation
Connectionism. Neural networks
Control systems
Dynamical systems
Exact sciences and technology
Feedback
Humans
Lyapunov functions
Lyapunov method
Mathematical analysis
Neural networks
Neural Networks (Computer)
neural networks (NNs)
Nonlinear control systems
Nonlinear Dynamics
Nonlinear systems
Output feedback
output feedback nonlinear systems
Programmable control
Signal Processing, Computer-Assisted
Sliding mode control
Stability
unknown functions
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Summary: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.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2010.2047115