Direct and Indirect Adaptive Inverse Control Design for Non-Minimum Phase EHA System with Periodic Disturbance via FASS-NLMS Algorithm

Abstract

In this paper, is performed the comparative analysis of performance between the Direct and Indirect Adaptive Inverse Control applied to a non-minimum phase Electro-Hydraulic (EHA) system in the presence of a periodic disturbance signal. The performance of an adaptive inverse controller is influenced by the trade-off between the convergence speed and the steady-state MSE during the update of the estimate of the weights vector. Aiming to propose a new optimization algorithm based on stochastic gradient descent, in this paper, a new version of the NLMS algorithm with adaptive step size is proposed, with the goal of obtaining a good trade-off between convergence speed and the steady-state MSE. For this, the step size is adapted by a Mamdani Fuzzy Inference System in function of the squared error and of the instant of normalized time by the Min-Max method. Computational results illustrate the efficiency of the proposed optimization algorithm in the design of these two approaches of adaptive inverse control.