Abstract
In this paper, we develop an event-based adaptive robust stabilization method for continuous-time nonlinear systems with uncertain terms via a self-learning technique called neural dynamic programming. Through system transformation, it is proven that the robustness of the uncertain system can be achieved by designing an event-triggered optimal controller with respect to the nominal system under a suitable triggering condition. Then, the idea of neural dynamic programming is adopted to perform the main controller design task by building and training a critic network. Finally, the effectiveness of the present adaptive robust control strategy is illustrated via a simulation example.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grants 61233001, 61273140, 61304086, 61402260, 61533017, and U1501251, in part by Beijing Natural Science Foundation under Grant 4162065, in part by Shandong Province Higher Educational Science and Technology Program (J13LN42), in part by The Excellent Young and Middle-Aged Scientist Award Foundation of Shandong Province (BS2013DX043), and in part by the Early Career Development Award of SKLMCCS.
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Wang, D., Ma, H., Liu, D., Wang, H. (2016). Neural Dynamic Programming for Event-Based Nonlinear Adaptive Robust Stabilization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_16
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DOI: https://doi.org/10.1007/978-3-319-46687-3_16
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