In this paper, a direct adaptive fuzzy-neural output-feedback controller (DAFOC) for a class of uncertain nonlinear systems is developed under the constraint that only the system output is available for measurement. An output feedback control law and an update law are derived for on-line tuning the weighting factors of the DAFOC. By using strictly positive-real Lyapunov theory, the stability of the closed-loop system compensated by the DAFOC can be verified. Moreover, the proposed overall control scheme guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.
ASJC Scopus subject areas
- Artificial Intelligence