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Output Tracking Control via Neural Networks for High-Order Stochastic Nonlinear Systems with Dynamic Uncertainties
- Source :
- International Journal of Fuzzy Systems. 23:716-726
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- This paper is concerned with the problem of output tracking control for a class of high-order stochastic nonlinear systems with dynamic uncertainties. The systems under investigation have dynamic uncertainties, unknown high-order terms, and uncertain nonlinear functions simultaneously. The packaged unknown nonlinearities are manipulated successful by using radial basis function neural networks. Two dynamic signals are introduced to dominate the dynamic uncertainties and adjust the tracking accuracy, respectively. The proposed continuous controller guarantees that all states of the closed-loop system are bounded in probability, and the tracking error converges to a preassigned range. Finally, a simulation example is provided to demonstrate the effectiveness of the control scheme.
- Subjects :
- Artificial neural network
Computer science
Computational intelligence
02 engineering and technology
Tracking (particle physics)
Theoretical Computer Science
Tracking error
Nonlinear system
Computational Theory and Mathematics
Artificial Intelligence
Control theory
Bounded function
0202 electrical engineering, electronic engineering, information engineering
Range (statistics)
020201 artificial intelligence & image processing
Software
Subjects
Details
- ISSN :
- 21993211 and 15622479
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- International Journal of Fuzzy Systems
- Accession number :
- edsair.doi...........5c3818b402243da04bf65a929a62045d