Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Lv, Maolong (TU Delft Team Bart De Schutter)
De Schutter, B.H.K. (TU Delft Team Bart De Schutter)
Cao, Jinde (Southeast University)
Baldi, S. (TU Delft Team Bart De Schutter; Southeast University)

2023

Practical tracking results have been reported in the literature for high-order odd-rational-power nonlinear dynamics (a chain of integrators whose power is the ratio of odd integers). Asymptotic tracking remains an open problem for such dynamics. This note gives a positive answer to this problem in the framework of prescribed performance control, without approximation structures (neural networks, fuzzy logic, etc.) being involved in the control design. The unknown system uncertainties are first transformed to unknown but bounded terms using barrier Lyapunov functions, and then these terms are compensated by appropriate adaptation laws. A method is also proposed to extract the control terms in a linear-like fashion during the control design, which overcomes the difficulty that virtual or actual control signals appear in a nonaffine manner. A practical poppet valve system is used to validate the effectiveness of the theoretical findings.

Asymptotic tracking
Control design
High-order odd-rational-power nonlinear systems
Hysteresis
Neural networks
Nonlinear dynamical systems
Prescribed performance
Quantization (signal)
Trajectory
Uncertainty

http://resolver.tudelft.nl/uuid:c8e12baf-f9fb-472b-8343-bfc930c2c2a5

https://doi.org/10.1109/TAC.2022.3147271

2023-07-01

0018-9286

IEEE Transactions on Automatic Control, 68 (2), 1047-1053

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Maolong Lv, B.H.K. De Schutter, Jinde Cao, S. Baldi