High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
Discrete-time decentralized neural block controller for a five DOF robot manipulator
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Real-time decentralized neural block controller for a robot manipulator
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Decentralized discrete-time neural control for a Quanser 2-DOF helicopter
Applied Soft Computing
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This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which 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 interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior 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 the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.