Abstract
This paper presents a method to synthesize the controller for uncertain nonlinear systems based on a combination of sliding mode control, adaptive control, and radial basis function (RBF) neural network. We propose an adaptive control law based on the RBF neural network to identify and compensate for variable parameter components, nonlinear function vectors, and external disturbance. The main linear component is built based on a sliding control. The designed controller has the advantage of being resistant to the elements of uncertainty and has a high control quality.
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Cuong, N.T.N., Chuong, L.V., Anh, M.T. (2022). Robust Adaptive Controller for a Class of Uncertain Nonlinear Systems with Disturbances. In: Banerjee, S., Saha, A. (eds) Nonlinear Dynamics and Applications. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-99792-2_59
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DOI: https://doi.org/10.1007/978-3-030-99792-2_59
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