Decentralized Neural Control Structure

A novel decentralized variable structure control approach for large scale uncertain systems is developed using Recurrent High Orfer Neural Networks (RHONN). It is assumed that each subsystem belongs to a class of block controllable nonlinear systems whose vector fields includes interconections terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate on-line the dynamical behavior of each nonlinear subsystem. The control law, which is able to ensure tracking of 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 the proposed algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.