Note:

Abstract: This paper proposes a method for frequency-weighted discrete-time linear parameter varying (LPV) model reduction with bounded rate of parameter variation, using structurally balanced truncation with a priori (non-tight) upper error bounds for each xed parameter. For systems with both input and output weighting lters, guaranteed stability of the reduced-order model is proved as well as existence of solutions, provided that the full-order model is stable. A technique based on cone complementarity linearization is proposed to solve the associated LMI problem. Application to the model of a gantry robot illustrates the effectiveness of the approach. Moreover, a method is proposed to make the reduced order model suitable for practical LPV controller synthesis.

Note:

Abstract: This paper proposes a method for frequency-weighted discrete-time linear parameter varying (LPV) model reduction with bounded rate of parameter variation, using structurally balanced truncation with a priori (non-tight) upper error bounds for each xed parameter. For systems with both input and output weighting lters, guaranteed stability of the reduced-order model is proved as well as existence of solutions, provided that the full-order model is stable. A technique based on cone complementarity linearization is proposed to solve the associated LMI problem. Application to the model of a gantry robot illustrates the effectiveness of the approach. Moreover, a method is proposed to make the reduced order model suitable for practical LPV controller synthesis.