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Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pérez, José P. |
| Copyright Year | 2014 |
| Abstract | This paper presents an application of adaptive neural networks based on a dynamic neural network to trajectory tracking of unknown nonlinear plants. The main methodologies on which the approach is based are recurrent neural networks and Lyapunov function methodology and Proportional-Integral (PI) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PI approach. The new control scheme is applied via simulations to Chaos Synchronization. Experimental results have shown the usefulness of the proposed approach for Chaos Production. To verify the analytical results, an example of a dynamical network is simulated and a theorem is proposed to ensure tracking of the nonlinear system. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.redalyc.org/pdf/615/61531305013.pdf |
| Alternate Webpage(s) | http://scielo.unam.mx/pdf/cys/v18n2/v18n2a13.pdf |
| Alternate Webpage(s) | http://www.scielo.org.mx/pdf/cys/v18n2/v18n2a13.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Anser rossii Artificial neural network Chen–Ho encoding Controllers Entity–relationship model Identifier Lyapunov fractal Neural Network Simulation Nonlinear system Optimal control Recurrent neural network Synchronization of chaos |
| Content Type | Text |
| Resource Type | Article |
