Abstract
This paper aims to compare the performance of Direct Adaptive Inverse Control (DAIC) and Indirect Adaptive Inverse Control (IAIC) applied to a non-minimum phase plant in the presence of a periodic disturbance signal added to the control signal. Besides the structural differences, the performance of DAIC and IAIC is influenced, during the update of the estimate of the controller weights vector, by the convergence speed and steady-state Mean Square Error (MSE). Thus, in this work a new adaptive algorithm based on stochastic gradient, entitled Fuzzy Variable Step Size Normalized Least Mean Square (FVSS-NLMS), is proposed. In the FVSS-NLMS algorithm, a Mamdani Fuzzy Inference System (MFIS) is used to adapt the step size of NLMS algorithm, with the objective of obtain a good performance in terms of convergence speed and steady-state MSE. The results obtained by the DAIC and IAC designed by the FVSS-NLMS algorithm were compared with versions of NLMS algorithm with fixed and variable step size.
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