Feedforward learning control for SISO plant with finite zeros and nonlinearity

This paper proposes a scheme for feedforward (FF) learning control based on scheduled locally weighted regression (S-LWR). For an unknown nonlinear single-input single-output (SISO) plant, it generates FF control signals based on local models of inverse dynamics identified by on-line S-LWR learning at a current operating point (called scheduling parameter). This scheme was proposed previously by the authors under the assumption that every linear approximation of the plant is free of finite zeros; i.e., the numerator of its transfer function is constant. The objective of this paper is to relax this restriction by providing adjustable FF controller poles to cancel the plant zeros. Numerical simulation is carried out to verify effectiveness of the propose scheme.