While the use of neural networks for learning has gained traction in control and system identification problems, their use in data-driven estimator design is not as prevalent. Prior art on neuro-adaptive observers limit the type of activation functions to radial basis function networks and provide conservative bounds on the resulting observer estimation error because they leverage boundedness of the activation functions rather than exploiting their underlying structure. This paper proposes the use of Lipschitz activation functions in the neuroadaptive observer: utilizing the Lipschitz constants of these activations simplifies the data-driven observer design procedure via recently discovered LMI conditions. Furthermore, in spite of measurement noise and approximation error, pre-computable robust stability guarantees are provided on the resulting state estimation error.
|Original language||English (US)|
|Title of host publication||2019 IEEE 58th Conference on Decision and Control, CDC 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Dec 2019|
|Event||58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France|
Duration: Dec 11 2019 → Dec 13 2019
|Name||Proceedings of the IEEE Conference on Decision and Control|
|Conference||58th IEEE Conference on Decision and Control, CDC 2019|
|Period||12/11/19 → 12/13/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.
- adaptive systems
- function approximation
- linear matrix inequalities
- machine learning
- neural networks
- nonlinear systems