Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Raul-Cristian Roman,Radu-Emil Precup,Emil Petriu,Mircea-Bogdan Radac

doi:10.3390/machines5040025

Raul-Cristian Roman, Radu-Emil Precup + Show 2 more

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https://doi.org/10.3390/machines5040025

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Abstract

This paper proposes the combination of two data-driven techniques, namely virtual reference feedback tuning (VRFT) and model-Free Control (MFC) in terms of the VRFT of MFC algorithms dedicated to servo systems. VRFT ensures the automatic optimal computation of the parameters of three MFC algorithms represented by intelligent proportional (iP), intelligent proportional-integral (iPI), and intelligent proportional-integral-derivative (iPID) controllers. The combination of MFC and VRFT leads to a novel mixed MFC-VRFT approach. The approach is validated by experimental results related to the angular speed control of modular servo system laboratory equipment. The performance of the control systems with the MFC algorithms (iP, iPI, and iPID controllers) tuned by the mixed MFC-VRFT approach is compared with that of control systems with MFC algorithms tuned by a metaheuristics gravitational search algorithm (GSA) optimizer, and of control systems with I, PI and PID controllers optimally tuned by VRFT and GSA in the same optimization problem.

Highlights

The feature of data-driven model-free control (MFC) [1] is that it uses only the input/output (I/O)data in order to control unknown processes
This paper has proposed a novel mixed MFC-virtual reference feedback tuning (VRFT) approach
This approach has been accompanied by the implementation and application of a set of controllers, i.e., VRFT-intelligent proportional (iP), VRFT-intelligent PI (iPI), and VRFT-intelligent PID (iPID)

Summary

Introduction

The feature of data-driven model-free control (MFC) [1] is that it uses only the input/output (I/O)data in order to control unknown processes. The feature of data-driven model-free control (MFC) [1] is that it uses only the input/output (I/O). MFC is known in the literature as model-free tuning, as it approximates the process model with a so called ultra-local model, and a rapidly adaptable estimator is used in the process approximation. The control system (CS) stability is guaranteed in MFC if certain constraints are fulfilled. Other model-free tuning techniques similar to MFC are active disturbance rejection control [14,15,16] and model-free adaptive control (MFAC) [17,18,19]

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Discussion

Conclusion