Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen
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| Natura: | Artículo científico |
| Lingua: | en |
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Instituto Politécnico Nacional
2014
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| _version_ | 1876427950993178624 |
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| author | Joel Perez |
| author_facet | Joel Perez |
| contents | Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen Joel Perez Jose P. Perez, Francisco Rodríguez Angel Flores Computación Lya PI control chaos production trajectory tracking chaos synchronization 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. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61531305013 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.18 |
| format | Artículo científico |
| id | redalyc_61531305013 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen Joel Perez Computación Lya PI control chaos production trajectory tracking chaos synchronization Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen Joel Perez Jose P. Perez, Francisco Rodríguez Angel Flores Computación Lya PI control chaos production trajectory tracking chaos synchronization 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. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61531305013 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.18 |
| title | Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen |
| topic | Computación Lya PI control chaos production trajectory tracking chaos synchronization |
| url | https://www.redalyc.org/articulo.oa?id=61531305013 |