Feedback Linearization Control of Nonlinear System †
Abstract
:1. Introduction
2. Brunovsky Canonical Form
- 1.
- There exists a known upper bound :
- 2.
- Function satisfies the condition:
3. Tracking Controller and Error Dynamics
4. Neural Networks for Approximating Functions
- a.
- The inequality restricting the state space vector is valid for the computed constants and : ;
- b.
- On any compact set, there are constants and that restrict the Euclidean norms of the approximated functions: .
5. Controller Structure
5.1. System with a Known Function
5.2. System with an Unknown Function
6. Modelling
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Neural network controller: | |
Robustifying Term: | |
Neural network weight update law: | |
Parameters: | |
Signals: |
Neural network controller: | |
Robustifying Term: | |
Neural network weight update laws: | |
Signals: | |
Parameters: |
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Trenev, I.S.; Devyatkin, D.D. Feedback Linearization Control of Nonlinear System. Eng. Proc. 2023, 33, 36. https://doi.org/10.3390/engproc2023033036
Trenev IS, Devyatkin DD. Feedback Linearization Control of Nonlinear System. Engineering Proceedings. 2023; 33(1):36. https://doi.org/10.3390/engproc2023033036
Chicago/Turabian StyleTrenev, Ivan Sergeevich, and Daniil Dmitrievich Devyatkin. 2023. "Feedback Linearization Control of Nonlinear System" Engineering Proceedings 33, no. 1: 36. https://doi.org/10.3390/engproc2023033036