Comparative Analysis of the Optimization and Implementation of Adjustment Parameters for Advanced Control Techniques

Cleber Asmar Ganzaroli, Douglas Freire de Carvalho, Antonio Paulo Coimbra, Luiz Alberto do Couto and Wesley Pacheco Calixto
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Cleber Asmar Ganzaroli: Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Goiania 74605-010, GO, Brazil
Douglas Freire de Carvalho: Studies and Researches in Science and Technology Group, Federal Institute of Goias, Senador Canedo 75250-000, GO, Brazil
Antonio Paulo Coimbra: Institute of Systems and Robotics, University of Coimbra, 3030-194 Coimbra, Portugal
Luiz Alberto do Couto: Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Goiania 74605-010, GO, Brazil
Wesley Pacheco Calixto: Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Goiania 74605-010, GO, Brazil

Energies, 2022, vol. 15, issue 11, 1-27

Abstract: This paper proposes the implementation, analysis and comparison of the control techniques Proportional, Integral and Derivative, Nonlinear Predictive, Fuzzy control and Sliding Mode Control technique applied to the speed control of an independent excited DC motor driven by a three-phase fully controlled rectifier of six pulses. The methodology proposes the design of the bench, modeling of the real system by the system identification method and the adjustments of the parameters of the controllers using an optimization process. Comparisons are made between the techniques, highlighting their characteristics and performances when executed under similar conditions. The robustness of each control, when acting on a nonlinear system, is investigated. All control techniques are applied in three different tests: (i) reference signal of step type without load application, (ii) reference signal with amplitude variation without load application and (iii) reference signal of step type with load application. The smallest value of the integral of the absolute percentage error for the first test is 2.01% with the Fuzzy control, for the second test is 3.34% with the nonlinear predictive control and for the third test it is 1.41% also with the nonlinear predictive control. The techniques present satisfactory performance in the execution of the proposed control, depending, therefore, on the analysis of the system to be implemented to determine the appropriate method.

Keywords: DC motor; fuzzy control; model predictive control; sliding mode control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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