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A Hybrid Neural-decoupling Pole Placement Controller and Its Application
| Content Provider | Semantic Scholar |
|---|---|
| Author | Henriques, J. Dourado, António |
| Copyright Year | 1999 |
| Abstract | A hybrid control architecture is proposed integrating recurrent dynamic neural networks into the pole placement context. The neural network topology involves a modi ̄ed recurrent Elman network to capture the dynamics of the plant to be controlled, being the learning phase implemented on-line using a truncated backpropagation through time algorithm. At each time step the neural model, modelling a general non-linear state space system, is linearized to produce a discrete linear time varying state space model. Once the neural model is linearised some well-established standard linear control strategies can be applied. In this work the design of a decoupling pole placement controller is considered at each instant, which combined with the on-line learning of the network results in a self-tuning adaptive control scheme. Experimental results collected from a laboratory three tank system con ̄rm the viability and e®ectiveness of the proposed methodology. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://control.dei.uc.pt/pdf/JH091999.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
