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 trajectory tracking 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 comparative experimental results, where a good convergent results, are obtained.
CITATION STYLE
Baruch, I. S., Beltran, R. L., Olivares, J. L., & Garrido, R. (2004). A fuzzy-neural multi-model for mechanical systems identification and control. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2972, pp. 774–783). Springer Verlag. https://doi.org/10.1007/978-3-540-24694-7_80
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