Analysis of Fractional Order-Adaptive Systems Represented by Error Model 1 Using a Fractional-Order Gradient Approach

• Published in: Mathematics (Basel) Vol. 12; no. 20; p. 3212
• Main Authors: Sánchez-Rivero, Maibeth, Duarte-Mermoud, Manuel A., Travieso-Torres, Juan Carlos, Orchard, Marcos E., Ceballos-Benavides, Gustavo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.10.2024
• Subjects: Adaptive control; Adaptive systems; Calculus; Convergence; Error analysis; Fractional calculus; fractional-order adaptive control (FOAC); fractional-order calculus (FOC); fractional-order steepest descend gradient (FOSDG); Integers; Machine learning; Operators (mathematics); Optimization; Parameter identification; Robust control; Stability; steepest descend gradient (SDG)
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12203212

In adaptive control, error models use system output error and adaptive laws to update controller parameters for control or identification tasks. Fractional-order calculus, involving non-integer-order derivatives and integrals, is increasingly important for modeling, estimation, and control due to its ability to generalize classical methods and offer improved robustness to disturbances. This paper addresses the gap in the literature where fractional-order gradient methods have not yet been extensively applied in identification and adaptive control schemes. We introduce a fractional-order error model with fractional-order gradient (FOEM1-FG), which integrates fractional gradient operators based on the Caputo fractional derivative. By using theoretical analysis and simulations, we confirm that FOEM1-FG maintains stability and ensures bounded output errors across a variety of input signals. Notably, the fractional gradient’s performance improves as the order, β, increases with β>1, leading to faster convergence. Compared to existing integer-order methods, the proposed approach provides a more flexible and efficient solution in adaptive identification and control schemes. Our results show that FOEM1-FG offers superior stability and convergence characteristics, contributing new insights to the field of fractional calculus in adaptive systems.