| Abstract |
Neural-Network (NN) based compensation is a thoroughly investigated topic in automatic control. However, these approaches include the neural network inside the control loop. This paper proposes an alternative approach where the NN is external to the loop, making decisions on the parameters of a linear compensator in cascade with a plant to be controlled with a feedback system. The proposed model utilizes the adept sequence transduction capabilities of the Transformer architecture. This approach is used to design a discrete controller of any order to maximize available performance. This paper applies this method to simple plants without extreme dynamics and a plant with non-minimum phase and very high quality factor modes. |
| Authors |
Ifan G. Hughes   , John F. O’Brien
|
| Journal Info |
Not listed | 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
|
| Publication Date |
6/28/2023 |
| ISSN |
Not listed |
Type |
article |
| Open Access |
closed
|
| DOI |
https://doi.org/10.1109/aim46323.2023.10196217 |
| Keywords |
Feedback Control (Score: 0.592066) , Nonlinear Control (Score: 0.569576) , Adaptive Control (Score: 0.55939) , Controller Tuning (Score: 0.557255) , Model Reduction (Score: 0.552126)
|
