2 research outputs found
Ga-based Adaptive Fuzzy-neural Control for A Class of Mimo Systems
[[abstract]]A GA-based adaptive fuzzy-neural controller for a class of multiinput multi-output nonlinear systems, such as robotic systems, is developed for
using observers to estimate time derivatives of the system outputs. The weighting parameters of the fuzzy-neural controller are tuned on-line via a genetic algorithm (GA). For the purpose of on-line tuning the weighting parameters of
the fuzzy-neural controller, a Lyapunov-based fitness function of the GA is obtained. Besides, stability of the closed-loop system is proven by using strictlypositive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees
that all signals involved are bounded and the outputs of the closed-loop system
track the desired output trajectories. Finally, simulation results are provided to
demonstrate robustness and applicability of the proposed method.