Run-time efficient observer-based fuzzy-neural controller for nonaffine multivariable systems with dynamical uncertainties

In this paper, a novel hierarchical structure with run-time efficiency is developed to solve the rule explosion problem of fuzzy-neural network control for a class of uncertain nonaffine multivariable systems. The parameters of the hybrid adaptive controller are on-line tuned by the derived update laws under the constraint that only system outputs are available for measurement. Compared with the previous approaches, the proposed design process is more flexible and requires less computation time. According to the stability analysis, the overall control scheme guarantees that the closed-loop systems can obtain successful system control, effective state observer, and desired tracking performance. Finally, illustrative examples are provided to show the effectiveness of the proposed approach.