H.T. Mok, C.W. Chan, and W.K. Yeung
PI controller, neurofuzzy networks, nonlinear system, adaptive control
PI controllers are still among the most popular industrial controllers, as they are relatively easy to install and reasonably robust, and can remove steady-state errors of type 0 processes. However, if the system is highly nonlinear, the performance of the PI controllers may deteriorate rapidly. A popular approach to control nonlinear systems is to switch between several linear PI controllers using fuzzy logic based on the Takagi-Sugeno model. In this paper, B- spline neurofuzzy networks are used to implement such a nonlinear controller. Neurofuzzy networks are chosen to implement the nonlinear PI controller for their abilities to approximate nonlinear functions and be trained online using experimental data. Design guidelines and training of the proposed controller are presented. The implementation of the proposed controller is presented, and its performance is demonstrated using a two-tank water level control rig and a continuous stirred-tank reactor process, and compared with PI controllers tuned using existing techniques.
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