Data-Driven Point-to-Point Finite-Iteration Learning Control for a Class of Nonlinear Systems With Output Saturation

This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considerin...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 55; H. 11; S. 5492 - 5503
Hauptverfasser: Bu, Xuhui, Yang, Chaohua, Lv, Lingling, Liang, Jiaqi, Hou, Zhongsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2025
Schlagworte:
Approximation algorithms
Control systems
Convergence
Date-driven control
finite-iteration learning control (FILC)
fractional power learning rule
Heuristic algorithms
Iterative methods
Nonlinear dynamical systems
point-to-point (PTP) tracking
Real-time systems
sensor saturation
Sensors
Trajectory
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:This article considers the point-to-point tracking control problem for a class of unknown nonlinear discrete-time systems with output saturation. A novel data-driven finite-iteration learning control algorithm is proposed to achieve bounded tracking errors within limited iteration. First, considering the case that the model of the nonlinear discrete-time system is unknown, the relationship between the output of the system and the control inputs at these given points is derived using recursive evolution in the time domain. Then, the dynamic data-driven model of the system is established using iterative domain dynamic linearization techniques. Second, a finite finite-iteration learning algorithm based on the fractional power of error information is designed, and the finite-iteration convergence of the proposed algorithm is rigorously proven in theory. Finally, the effectiveness of the proposed method is validated by simulation results.
Bibliographie:ObjectType-Article-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2025.3597743