Disturbance Rejection in Nonlinear Uncertain Systems Using Feedforward Control

The paper considers disturbance rejection in uncertain nonlinear systems in which the disturbance changes the relationship between the control signal and the controlled variable, and the measurement of the disturbance is inaccurate. A feedforward control scheme is proposed that uses a direct adaptive controller based on a fuzzy relational model identified using feedback error learning. A two-stage learning scheme is proposed to identify an ancillary fuzzy relational model of the controller, whose fuzzy output gives an indication of the uncertainties resulting from measurement. The output of the controller is conditionally defuzzified so as to take account of these uncertainties and reduce the control activity. The focus is on the design of a controller for disturbance rejection in nonlinear uncertain systems, where the measurement noise is in the same frequency range as the disturbance, and removing it using low-pass filtering is not an option.

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