On the design and analysis of structured-ANN for online PID-tuning to bulk resumption process in ore mining system

Abstract

The tuning of proportional–integral–derivative (PID) controllers has been extensively used in the industry, this adjustment is performed by means of conventional methods, such as the Ziegler–Nichols and trial-and-error that often fails to address inherent complexity of industrial processes. To overcome these problems of PID tuning, a neurocomputing adaptive novel methodology is presented in this paper. The proposed methodology is based on a structured artificial neural network (S-ANN) and a propagation matrix of PID actions that was developed to support the tuning. The problem formulation and S-ANN-based solution are tightly connected, i.e., the nodes and layers of S-ANN topology are ruled by the order of the propagation matrix. For given operational conditions of the plant and disturbance in its parameters, the performance of the trained network is evaluated for tasks within the scope of learning on a bulk resumption process in ore mining system, focusing the control system output accuracy and convergence of the S-ANN algorithm.