Adaptive Neural Control for Output Feedback Nonlinear Systems Using a Barrier Lyapunov Function
National University of Singapore · Agency for Science, Technology and Research · +1 more institution
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 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…
Citation impact
- FWCI
- 28.43
- Percentile
- 100%
- References
- 30
Authors
4Topics & keywords
- Control theory (sociology)
- Nonlinear system
- Adaptive control
- Artificial neural network
- Lyapunov function
- Computer science
- Lyapunov redesign
- Output feedback