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Adaptive output feedback control of nonlinear systems with prescribed performance and MT-filters
- Source :
- Neurocomputing. 207:717-725
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- In this paper, adaptive prescribed performance output feedback control is investigated for a class of nonlinear systems with unmodeled dynamics. Neural networks are used to approximate the unknown nonlinear functions. MT-filters are employed to estimate the unmeasured states. The unmodeled dynamics is dealt with by introducing an available dynamic signal. Adaptive output feedback dynamic surface control and parameter adaptive laws are proposed based on introducing the prescribed performance function and output error transformation. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed approach.
- Subjects :
- 0209 industrial biotechnology
Artificial neural network
Computer science
Cognitive Neuroscience
Control (management)
02 engineering and technology
Nonlinear control
Signal
Computer Science Applications
Nonlinear system
020901 industrial engineering & automation
Transformation (function)
Artificial Intelligence
Control theory
Bounded function
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Simulation
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 207
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........cb1374ddd6bc314432e4618525df7019