Models for Optimal Online Tuning Based on Computational Intelligence of PID Controllers Applied to Operational Processes of Bulk Reclaimers

  • José Pinheiro de MouraEmail author
  • João Viana da Fonseca Neto
  • Patrícia Helena M. Rêgo


This paper considers the development of models for the optimal online tuning of PID controllers based on computational intelligence approaches that are applied to the operational processes of bulk reclaimers. In the first instance, the optimal gains of a PID controller are determined using a structured artificial neural network (ANN), and in the second instance, a fuzzy system is used to carry out online adjustment through a real-time gain scheduling scheme. The bulk resumption process consists of picking up the stored material in stacks and transporting it using conveyor belts for shipment. For the control system, a model based on data pertaining to the electric current in a bucket wheel motor (a device that picks up material) is estimated and compared with the load measured using a scale. The difference between the load estimated by the model and that measured by the scale is the error, and the proposed control system is designed to minimize it. The results of simulations show that the controller models performed better using structured ANNs and fuzzy logic than PID controllers tuned by the second Ziegler–Nichols method, and the PID–fuzzy controller proposed by Zhao and Tomizuka.


Computational intelligence PID controllers Bulk reclaimers Artificial neural network Fuzzy system 



We thank PPGEE of the Federal University of Maranhão for the technical/scientific and practical lessons. We are especially grateful to FAPEMA for encouraging high-level research in the State of Maranhão. We also thank the Department of Physics of the State University of Maranhão for making this research feasible. We thank CAPES for promoting and supporting advanced research that contributed to this work. Finally, the Vale S.A. Company for providing its specialists for practical guidance for the execution of the experiments.


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Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  • José Pinheiro de Moura
    • 1
    • 2
    Email author
  • João Viana da Fonseca Neto
    • 1
    • 2
  • Patrícia Helena M. Rêgo
    • 1
  1. 1.UEMA - Cidade Universitária Paulo VISão LuísBrazil
  2. 2.UFMA - Cidade Universitátia Dom DelgadoSão LuísBrazil

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