Abstract
An adaptive output feedback control methodology is developed for a class of uncertain multi-input multi-output nonlinear systems using linearly parameterized neural networks. The methodology can be applied to non-minimum phase systems if the non-minimum phase zeros are modeled to a sufficient accuracy. The control architecture is comprised of a linear controller and a neural network. The neural network operates over a tapped delay line of memory units, comprised of the system's input/output signals. The adaptive laws for the neural-network weights employ a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of an inverted pendulum on a cart illustrate the theoretical results.
Original language | English (US) |
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Pages (from-to) | 513-522 |
Number of pages | 10 |
Journal | Automatica |
Volume | 42 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2006 |
Externally published | Yes |
Keywords
- Adaptive control
- Neural networks
- Output feedback
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering