Quasi‐ARX neural network based adaptive predictive control for nonlinear systems
| Autor: | Mohd Hamiruce Marhaban, Mohammad Abu Jami‘in, Imam Sutrisno, Jinglu Hu, Norman Mariun |
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| Rok vydání: | 2015 |
| Předmět: |
Lyapunov stability
Artificial neural network Estimation theory Computer science 020208 electrical & electronic engineering Estimator 02 engineering and technology Tracking error Model predictive control Nonlinear system Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering |
| Zdroj: | IEEJ Transactions on Electrical and Electronic Engineering. 11:83-90 |
| ISSN: | 1931-4981 1931-4973 |
| DOI: | 10.1002/tee.22191 |
| Popis: | In this paper, a new switching mechanism is proposed based on the state of dynamic tracking error so that more information will be provided –not only the error but also a one up to pth differential error will be available as the switching variable. The switching index is based on the Lyapunov stability theory. Thus the switching mechanism can work more effectively and efficiently. A simplified quasi-ARX neural-network (QARXNN) model presented by a state-dependent parameter estimation (SDPE) is used to derive the controller formulation to deal with its computational complexity. The switching works inside the model by utilizing the linear and nonlinear parts of an SDPE. First, a QARXNN is used as an estimator to estimate an SDPE. Second, by using SDPE, the state of dynamic tracking error is calculated to derive the switching index. Additionally, the switching formula can use an SDPE as the switching variable more easily. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance-rejection performances. Experimental results demonstrate its effectiveness. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. |
| Databáze: | OpenAIRE |
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