Abstract
This paper presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set of basis functions that respect the monotonic property of the system nonlinearities with respect to control input, a state predictor is defined for derivation of the adaptive laws. The adaptive dynamic inversion controller is defined as a solution of a fast dynamical equation, which achieves time-scale separation between the state predictor and the controller dynamics. Lyapunov-based adaptive laws ensure that the predictor tracks the state of the nonlinear system with bounded errors. As a result, the system state tracks the desired reference model with bounded errors. Benefits of the proposed design method are demonstrated using Van der Pol dynamics with nonlinear control input.
Original language | English (US) |
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Pages (from-to) | 1702-1711 |
Number of pages | 10 |
Journal | IEEE Transactions on Neural Networks |
Volume | 19 |
Issue number | 10 |
DOIs | |
State | Published - 2008 |
Keywords
- Adaptive control
- Nonaffine systems
- Time-scale separation
- Ultimate boundedness
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence