Adaptive neuro-fuzzy PID controller for nonlinear drive system. Issue 3 (5th May 2015)

• Record Type: Journal Article
• Authors: Derugo, Piotr; Szabat, Krzysztof
• Editors: Demenko, Ivo Doležel, Kay Hameyer, Wojciech Pietrowski and Krzysztof Zawirski, Andrzej
• Is Part Of: Compel. Volume 34:Issue 3(2015)
• Page Start: 792
• Page End: 807
• Publication Date: 2015-05-05
• Subjects: Adaptive -- Friction and backlash -- Fuzzy controller -- MRAS -- Neuro-fuzzy -- Parameters design; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-10-2014-0257
• Languages: English
• ISSNs: 0332-1649

Abstract

Abstract : Purpose: – Various control structures and approaches are in use nowadays. Development of new ideas allows to obtain better quality in control of different industrial processes and hence better quality of products. As it may seem that everything in the classical systems has already been discovered, more and more research centres are tending to incorporate fuzzy or neural control systems. The purpose of this paper is to present an application of the adaptive neuro-fuzzy PID speed controller for a DC drive system with a complex nonlinear mechanical part. Design/methodology/approach: – The model of the driven object including such elements as nonlinear shaft with backlash and friction has been modelled using Matlab-Simulink software. Afterwards experimental verification has been made using a dSPACE control card and experimental system with two DC motors connected with an elastic shaft. Findings: – The presented study shown that the adaptive controller is able to damp the torsional vibration effectively even for the wide range of the system nonlinearities. What is more the design approach for controllers design parameters has been described. Proposed approach is based on requested properties of system. Using proposed tuning scheme no detailed information about the object are needed. Originality/value: – This paper presents for the first time fully an PID adaptive neuro-fuzzy controller. The inputs are the weighted tracking error, error's derivative and integrated error. What is more the adaptation algorithm consists of a model tracking error its derivative and integer. Also the proposed tuning algorithm in such a form is an original outcome.