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Abstract
This paper addresses the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for regulating the wheat drying process. Given the nonlinear, inertial, and multi-coupled nature of the process, along with significant uncertainty in initial moisture content and technological parameters, conventional control methods fail to provide adequate performance. To reduce the size of the rule base, the proposed approach employs two independents but interrelated ANFIS blocks — a moisture controller and a temperature-limiting controller — reducing the number of rules from 625 to a total of 50. Controller parameters are tuned using Gaussian membership functions, a five-layer Takagi-Sugeno architecture, and a hybrid learning algorithm combining the least squares method with gradient descent. Control quality is evaluated using RMSE, MAE, IAE, ISE, and ITAE criteria, settling time, overshoot, and specific energy consumption. The results demonstrate the ANFIS controller's robustness to initial moisture uncertainty (16–30%) and its advantages over conventional control methods.
References
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