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Fuzzy neural network based adaptive control for a class of uncertain nonlinear
1. IEEE 2014 JAVA PROJECT
FUZZY NEURAL NETWORK-BASED ADAPTIVE CONTROL FOR A CLASS OF UNCERTAIN
NONLINEAR STOCHASTIC SYSTEMS
ABSTRACT:
This paper studies an adaptive tracking control for a class of nonlinear stochastic systems
with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it
is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks
are used to approximate unknown functions. Based on the backstepping design technique, the
controllers and the adaptation laws are obtained. Compared to most of the existing stochastic
systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce
online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-
loop system are semiglobally uniformly ultimately bounded in probability and the system output
tracks the reference signal to a bounded compact set. The simulation example is given to illustrate
the effectiveness of the proposed control algorithm.
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Mail to: ecwaytechnologies@gmail.com.
