Abstract
In this paper, an optimal sliding mode control based on neural network and genetic algorithm are designed for a DC motor velocity. The classical sliding mode control (SMC) can be used for the considered system. However, it presents some drawbacks of chattering, due to the higher needed switching gain in the case of large uncertainties. In order to reduce this gain, the neural network is used for the prediction of model unknown parts and hence enable a lower switching gain to be used. The neural network (NN) is used to improve the nominal model and then reduce the model uncertainties. This enables the sliding mode technique to be used without any chattering problems. The genetic algorithm is used in this study to optimize both, the learning rate of backpropagation algorithm used by the neural network and the variable switching gain of the SMC. The performance of the proposed approach is investigated in simulations by the comparison of the proposed approach with the classical sliding mode control technique.
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Sossé Alaoui, M.C., Satori, H., Satori, K. (2020). Optimal Sliding Mode Control of a DC Motor Velocity Based on Neural Network and Genetic Algorithm. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_20
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DOI: https://doi.org/10.1007/978-981-15-0947-6_20
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