Abstract
Identification and control for nonlinear dynamic systems still remains a challenging issue. Neural networks based models and controllers have often been effective in representing fields. In this paper, identification capability of radial basis function neural networks (RBFNN) is integrated into nonlinear internal model control to obtain a new control strategy. The model of inverse of the process is identified by training RBFNN. The NIMC controller consists of a model inverse controller and a robustness filter. At last, the simulation results for a continuous stirred tank reactor process demonstrate the advantage of the performance of the new strategy over that of a conventional PID controller.
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