A fuzzy-neural multi-model for mechanical systems identification and control
Ieroham Solomon Baruch, R. Beltran L, Ruben A. Garrido, Elena Gortcheva · 2004
The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with friction. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by a fuzzy rule based control system. The applicability of the proposed intelligent control system is confirmed by simulation and experimental results, where a good convergence of all recurrent neural networks, is obtained.