PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization
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| Format: | Artículo científico |
| Language: | en |
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Instituto Politécnico Nacional
2015
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| _version_ | 1876446259621920768 |
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| author | Joel Perez P. |
| author_facet | Joel Perez P. |
| contents | PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization Joel Perez P. Jose P. Perez Computación time time Lyapunov Lyapunov PID control This paper presents an application of Time- Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlin- ear plants. Our approach is based on two main method- ologies: the first one employs Time-Delay neural net- works and Lyapunov-Krasovskii functions and the sec- ond one is Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The new control scheme is applied via simulations to Chaos Synchroniza- tion. 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 the tracking of the nonlinear system. 2015 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61539886015 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.19 |
| format | Artículo científico |
| id | redalyc_61539886015 |
| institution | Redalyc |
| language | en |
| publishDate | 2015 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization Joel Perez P. Computación time time Lyapunov Lyapunov PID control PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization Joel Perez P. Jose P. Perez Computación time time Lyapunov Lyapunov PID control This paper presents an application of Time- Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlin- ear plants. Our approach is based on two main method- ologies: the first one employs Time-Delay neural net- works and Lyapunov-Krasovskii functions and the sec- ond one is Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID approach. The new control scheme is applied via simulations to Chaos Synchroniza- tion. 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 the tracking of the nonlinear system. 2015 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61539886015 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.2 Vol.19 |
| title | PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization |
| topic | Computación time time Lyapunov Lyapunov PID control |
| url | https://www.redalyc.org/articulo.oa?id=61539886015 |