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Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation
- Source :
- IEEE Transactions on Cybernetics; September 2023, Vol. 53 Issue: 9 p5957-5969, 13p
- Publication Year :
- 2023
-
Abstract
- Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov’s differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua’s oscillator are offered to verify the effectiveness of the proposed control algorithm.
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 53
- Issue :
- 9
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Periodical
- Accession number :
- ejs63808498
- Full Text :
- https://doi.org/10.1109/TCYB.2022.3222351