This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.
|Number of pages||24|
|Journal||International Journal of Robust and Nonlinear Control|
|Publication status||Published - 2023 Jan 25|
Bibliographical noteFunding Information:
National Natural Science Foundation of China, Grant/Award Numbers: 62003052; 62222310; 61973131; National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT), Grant/Award Number: NRF‐2020R1A2C1005449 Funding information
This work was partially supported by the National Natural Science Foundation of China (62003052) and the PhD Start‐up Fund of Liaoning Province (2020‐BS‐239).
information National Natural Science Foundation of China, Grant/Award Numbers: 62003052; 62222310; 61973131; National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT), Grant/Award Number: NRF-2020R1A2C1005449
© 2022 John Wiley & Sons Ltd.
- adaptive fixed-time control
- interconnected nonlinear systems
- reduced-order observer
- time-varying output constraints
ASJC Scopus subject areas
- Control and Systems Engineering
- Chemical Engineering(all)
- Biomedical Engineering
- Aerospace Engineering
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering