期刊
FACTA UNIVERSITATIS-SERIES MECHANICAL ENGINEERING
卷 20, 期 1, 页码 21-36出版社
UNIV NIS
DOI: 10.22190/FUME220111005P
关键词
Closed-loop data-driven system identification; Data-driven fuzzy control
资金
- Romanian Ministry of Education and Research, CNCS-UEFISCDI, within PNCDI III [PN-III-P4-ID-PCE-2020-0269, PN-III-P1-1.1-TE-2019-1117, PN-III-P1-1.1-PD-2019-0637]
- Politehnica University of Timisoara, Romania [CNFIS-FDI-2021-0582]
- NSERC of Canada
This paper proposes a low-cost data-driven fuzzy control method for servo systems, which achieves position control of nonlinear servo systems through system identification and controller tuning using open-loop and closed-loop data.
Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors' results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) openloop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach. Extended Symmetrical Optimum method, Servo systems
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