On the Approximation of Moments for Nonlinear Systems

Model reduction by moment-matching relies upon the availability of the so-called moment. If the system is nonlinear, the computation of moments depends on an underlying specific invariance equation, which can be difficult or impossible to solve. This article presents four technical contributions related to the theory of moment matching: first, we identify a connection between moment-based theory and weighted residual methods. Second, we exploit this relation to provide an approximation technique for the computation of nonlinear moments. Third, we extend the definition of nonlinear moment to the case in which the generator is described in explicit form. Finally, we provide an approximation technique to compute the moments in this scenario. The results are illustrated by means of two examples.

[1]  John V. Ringwood,et al.  Energy-maximising control of wave energy converters using a moment-domain representation , 2018, Control Engineering Practice.

[2]  Alessandro Astolfi,et al.  Characterization of the moments of a linear system driven by explicit signal generators , 2015, 2015 American Control Conference (ACC).

[3]  T. A. Zang,et al.  Spectral methods for fluid dynamics , 1987 .

[4]  Alessandro Astolfi,et al.  Model Reduction by Matching the Steady-State Response of Explicit Signal Generators , 2016, IEEE Transactions on Automatic Control.

[5]  Alessandro Astolfi,et al.  Model Reduction by Moment Matching for Linear and Nonlinear Systems , 2010, IEEE Transactions on Automatic Control.

[6]  Alessandro Astolfi,et al.  A Geometric Characterization of the Persistence of Excitation Condition for the Solutions of Autonomous Systems , 2017, IEEE Transactions on Automatic Control.

[7]  Athanasios C. Antoulas,et al.  Approximation of Large-Scale Dynamical Systems , 2005, Advances in Design and Control.

[8]  Emmanuel Trélat,et al.  Nonlinear Optimal Control via Occupation Measures and LMI-Relaxations , 2007, SIAM J. Control. Optim..

[9]  Alessandro Astolfi,et al.  Model Reduction by Moment Matching for Linear and Nonlinear Systems , 2010, IEEE Transactions on Automatic Control.

[10]  A. Astolfi Disturbance Attenuation and H,-Control Via Measurement Feedback in , 1992 .

[11]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..

[12]  John V. Ringwood,et al.  Moment-based constrained optimal control of an array of wave energy converters , 2019, 2019 American Control Conference (ACC).

[13]  Alessandro Astolfi,et al.  Moments of Random Variables: A Systems-Theoretic Interpretation , 2019, IEEE Transactions on Automatic Control.

[14]  Eduardo D. Sontag,et al.  Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .

[15]  Christopher I. Byrnes,et al.  Steady-state behaviors in nonlinear systems with an application to robust disturbance rejection , 2008, Annu. Rev. Control..

[16]  Alessandro Astolfi,et al.  Nonlinear Model Reduction by Moment Matching , 2017, Found. Trends Syst. Control..

[17]  P. Halmos Introduction to Hilbert Space: And the Theory of Spectral Multiplicity , 1998 .

[18]  Nahum Shimkin,et al.  Nonlinear Control Systems , 2008 .

[19]  Alessandro Astolfi,et al.  Moment-Based Discontinuous Phasor Transform and its Application to the Steady-State Analysis of Inverters and Wireless Power Transfer Systems , 2016, IEEE Transactions on Power Electronics.

[20]  Giordano Scarciotti,et al.  Approximation, analysis and control of large-scale systems : theory and applications , 2016 .