DECENTRALIZED ADAPTIVE CONTROL FOR A CLASS OF NONLINEAR CONTINUOUS TIME INTERCONNECTED SYSTEMS USING NEURAL NETWORKS
S. Baqqali and M. Makoudi
Keywords
Interconnected systems, decentralized adaptive control, nonlinear systems, neural networks, interconnection prediction
Abstract
The authors present a completely decentralized adaptive control for
a class of nonlinear, continuous time, interconnected systems, in
the sense that no information exchange between the subsystems is
needed. The main idea consists of predicting the interconnection
output terms using the polynomial series. These predictions are
then used in the local control law, which offers a general solution
in the area of interconnected linear and nonlinear systems with
arbitrary interconnections. In the present work, we consider the
case of controlling a class of interconnected systems using neural
networks. A multilayer neural network is used to model each
unknown subsystem and generate the control law. Based on the
error between the plant output and the model output, the weights
of the neural network are updated online according to a gradient
learning rule with dead zone. Finally, the results are illustrated by
numerical examples.
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