Dynamic Recurrent Neural Network Based Indirect Adaptive Control for Nonlinear Systems | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 6, Volume 26, Issue 1, January 2017, Page 79-98 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2017.63423 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohamed A. Hussien; Tarek A. Mahmoud; Mohamed I. Mahmoud | ||||
| Dept. of Industrial Electronics and Control Eng., Faculty of Electronic Engineering, Menoufia University, Egypt | ||||
| Abstract | ||||
| In this study, an indirect adaptive controller based on dynamic recurrent neural network (DRNN) is developed in the form of the internal model control structure. This control method includes two learning phases, i.e., off-line and on-line learning. In the offline learning, the DRNN is learned by the epoch wise back propagation through time (BPTT) method to represent the forward dynamics of the system to be controlled. In the online phase, the DRNN is used as the internal model of the controlled system and its parameters can be trained by the Truncated BPTT to cope with the possible change in the system dynamics. Hence, the mathematical inversion of the DRNN internal model is computed online to act as the forward controller. Finally, the controller is then obtained by cascading this inverse model with a robust filter and a linear compensator to improve the closed loop performance. The proposed method is applied to a continuous stirred tank reactor (CSTR) with time varying behavior to evaluate the performance of controller. | ||||
| References | ||||
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