2 research outputs found
Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator
In this work, we introduce an adaptive neural network controller for a class
of nonlinear systems. The approach uses two Radial Basis Functions, RBF
networks. The first RBF network is used to approximate the ideal control law
which cannot be implemented since the dynamics of the system are unknown. The
second RBF network is used for on-line estimating the control gain which is a
nonlinear and unknown function of the states. The updating laws for the
combined estimator and controller are derived through Lyapunov analysis.
Asymptotic stability is established with the tracking errors converging to a
neighborhood of the origin. Finally, the proposed method is applied to
control and stabilize the inverted pendulum system
STABLE ADAPTIVE CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITHOUT USE OF A SUPERVISORY TERM IN THE CONTROL LAW
In this paper, a direct adaptive control scheme for a class of nonlinear systems is proposed. The architecture employs a Gaussian radial basis function (RBF) network to construct an adaptive controller. The parameters of the adaptive controller are adapted and changed according to a law derived using Lyapunov stability theory. The centres of the RBF network are adapted on line using the k-means algorithm. Asymptotic Lyapunov stability is established without the use of a supervisory (compensatory) term in the control law and with the tracking errors converging to a neighbourhood of the origin. Finally, a simulation is provided to explore the feasibility of the proposed neuronal controller design method