Abstract
AbstractThis work addresses to perform the direct adaptive inverse control (DAIC) design via fractional least mean square (FLMS) algorithm. Since the controller of DAIC aims to track the plant inverse dynamics as a function of the plant model inverse identification, to track nonlinear dynamics of polynomial type, in this work, the controller is based on Volterra model. Since the performance of an estimation algorithm is important to update the estimate of the controller weight vector, the main objective of this work is to perform the performance analysis of FLMS algorithm, with respect to convergence speed and mean square error (MSE). The proposed analysis was performed on a model containing a polynomial type nonlinearity, represented by a Nonlinear AutoRegressive with eXogenous inputs (NARX) model. In addition, the proposed analysis was performed in the presence of a sinusoidal disturbance signal and time-varying reference signal.KeywordsControl based on modelAdaptive inverse controlFLMSNonlinear controlInverse modelVolterra series
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