Abstract
A new Modular Recurrent Trainable Neural Network (MRTNN) has been used for system identification of a vehicle motor system. The first MRTNN module identified the exponential part of the unknown vehicle motor plant and the second one - the oscillatory part of that plant. The vehicle motor plant has been controlled by an indirect sliding mode adaptive control system with integral term. The sliding mode controller used the estimated parameters and states to suppress the vehicle plant oscillations and the static plant output control error is reduced by an I-term added to the control.
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Baruch, I., Hernandez-Manzano, SM., Moreno-Cruz, J. (2013). Recurrent Neural Identification and I-Term Sliding Mode Control of a Vehicle System Using Levenberg-Marquardt Learning. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_27
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DOI: https://doi.org/10.1007/978-3-642-37798-3_27
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