An Incremental Learning-based Fuzzy Control Scheme for a Class of Uncertain Euler-Lagrange Systems

Document Type : Research Paper

Authors

1 Faculty of Electrical and Computer Engineering, University of Tabriz

2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Abstract

Euler-Lagrange systems describe a wide range of mechanical and robotic systems. Uncertainties and dynamic parameter
changes pose significant challenges to control the Euler-Lagrange systems using traditional methods. In such
cases, intelligent methods can cover the limits of the classical techniques. In this research, we intend to present a
fuzzy logic-based controller trained by the proposed incremental learning algorithm to face the challenges of Euler-
Lagrange systems. Incremental learning aims to accumulate experiences over time to train the models. We have
performed several simulations to test the capabilities of the proposed method and compared the results with wellknown
machine learning-based methods using various criteria. Considering the integral of absolute error, the results
show that the proposed method has improved by 40.89%, 38.32%, 34.12%, and 34.79% compared to the best other
method in nominal system scenario and three other scenarios considering three different levels of uncertainty. The
overshoot of the system response achieved by the proposed control scheme is approximately 44 − 48% less than the
best other method in four scenarios. Also, we have studied the system response to disturbance and noise.

Keywords

Main Subjects

References

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Iranian Journal of Fuzzy Systems
Volume 22, Issue 2
March and April 2025
Pages 147-170

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