Back to Search
Start Over
Decentralized Adaptive Command Filtered Neural Tracking Control of Large-Scale Nonlinear Systems: An Almost Fast Finite-Time Framework
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 32:3621-3632
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the "singular" and "explosion of complex" problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme.
- Subjects :
- Computer Networks and Communications
Computer science
Stability (learning theory)
02 engineering and technology
Residual
Computer Science Applications
Nonlinear system
Artificial Intelligence
Control theory
Adaptive system
Bounded function
Backstepping
0202 electrical engineering, electronic engineering, information engineering
Trajectory
020201 artificial intelligence & image processing
Software
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....facc78f485c00d509fd2e5b31611703e