Abstract
A novel decentralized variable structure neural control approach for large-scale uncertain systems is developed using Recurrent High Order Neural Networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnections terms. The interconnection terms are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law, are propposed in order to approximate on-line the dynamical behaviour of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of proposed decentralized identification and control algorithm is illustrated via simulations as applied to stabilize an interconnected double inverted pendulum.
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