Control of a second order system based on neural networks
DOI:
https://doi.org/10.21754/tecnia.v19i2.355Keywords:
neurocontroller, control with neural networks, booster neurocontroller, neural controllerAbstract
The present work shows the simulation and implementation of a "Neurocontroller" in a second order plant. The neural controller, also known as Neurocontroller, was implemented with a multilayer network, where the backpropagation of the error was developed through the "Backprogation" algorithm. The multilayer network, composed of a hidden layer and an output layer, was first simulated in Matlab to get the variation parameters, then it was simulated in Visual C++ to achieve the optimization. The architecture of this multilayer network was varied many times until reaching an optimal form that will be shown as the final architecture. Next, the simulation was done in LabVIEW 8.4, corroborating the simulations in Visual C++. Finally, the neural controller developed in LabVIEW was tested in real time, showing gratifying results and verifying its effectiveness despite simultaneous changes in the parameters.
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[1] Valverde, R., Gachet, D., Salichs, M. A., "Dynamic systems identification using rbf neural networks", área de ingeniería de sistemas y automática Universidad Carlos III de Madrid, Spain, pp 1-10. España, 1999.
[2] Önder Efe, M., Kaynak, O., Yu, X., Wilamowski, B. M., "Sliding mode control of nonlinear systems using gaussian radial basis function neural networks", pp 1-6. USA 2001.
[3] Valverde, R., "Neurocontrol of continuous processes through reinforcement learning". Universidad Carlos III de Madrid. C/butarque, 15. 28911, Leganés, pp 1-5. España, 1998.
[4] Valverde, R., "Control de sistemas mediante redes neuronales". Aprendizaje por refuerzo, Universidad Carlos III de Madrid, Tesis doctoral, pp 8-16. España, 1999.
[5] Werbos, P. J., "Neural networks applications". 1997 iop publishing ltd and Oxford University
Press, Handbook of Neural Computation release 97/1 f1.9:1-f1.9:10, pp 1 -10. USA 1997.
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