Abstract
In this paper, a new system identification scheme is proposed based on a memory-based modeling (MBM) method. According to the MBM method, some local models are automatically generated using input/output data pairs of the controlled object stored in the data-base. Especially, it is well known that the MBM method works suitably on nonlinear systems. Therefore, even if the nonlinearities are contained in the controlled object, accuracy identification can be performed by the proposed method. Moreover, since the parameter estimates are easily applied to many existing controllers, the good control result can be obtained for nonlinear systems. In this paper, the generalized predictive control (GPC) is used as the one of existing controllers, because the GPC is designed based on multi-step prediction, and is effective for systems with large, ambiguous and/or time-variant time-delays. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on some simulation examples.
