Neural Learning Control of Strict-Feedback Systems Using Disturbance Observer
| Autor: | Yingxin Shou, Jun Luo, Bin Xu, Huayan Pu, Zhongke Shi |
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| Rok vydání: | 2019 |
| Předmět: |
Scheme (programming language)
Disturbance (geology) Artificial neural network Computer Networks and Communications Computer science Control (management) 02 engineering and technology Tracking (particle physics) Computer Science Applications Nonlinear system Artificial Intelligence Control theory Disturbance observer 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing computer Software computer.programming_language |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 30:1296-1307 |
| ISSN: | 2162-2388 2162-237X |
| DOI: | 10.1109/tnnls.2018.2862907 |
| Popis: | This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good "understanding" of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained. |
| Databáze: | OpenAIRE |
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