Dissipativity preserving balancing for nonlinear systems - A Hankel operator approach

Abstract In this paper we present a version of balancing for nonlinear systems which is dissipative with respect to a general quadratic supply rate that depends on the input and the output of the system. We discuss an approach that allows us to apply the theory of balancing based upon Hankel singular value analysis. In order to do that we prove that the available storage and the required supply of the original system are the controllability and the observability functions of a modified, asymptotically stable, system. Then Hankel singular value theory can be applied and the axis singular value functions of the modified system equal the nonlinear extensions of “similarity invariants” obtained from the required supply and available storage of the original system. Furthermore, we also consider an extension of normalized comprime factorizations and relate the available storage and required supply with the controllability and observability functions of the factorizations. The obtained relations are used to perform model order reduction based on balanced truncation, yielding dissipative reduced order models for the original systems. A second order electrical circuit example is included to illustrate the results.

[1]  W.S. Gray,et al.  Nonlinear balanced realizations , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[2]  Sijbren Weiland Theory of approximation and disturbance attenuation for linear systems. , 1991 .

[3]  R. Ober Balanced parametrization of classes of linear systems , 1991 .

[4]  T. C. Ionescu,et al.  Balanced truncation for dissipative and symmetric nonlinear systems , 2005 .

[5]  J.M.A. Scherpen Duality and singular value functions of the nonlinear normalized right and left coprime factorizations. , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[6]  J. Willems Least squares stationary optimal control and the algebraic Riccati equation , 1971 .

[7]  Jacquelien M. A. Scherpen,et al.  Balanced Realization and Model Order Reduction for Nonlinear Systems Based on Singular Value Analysis , 2010, SIAM J. Control. Optim..

[8]  Duncan McFarlane,et al.  Balanced canonical forms for minimal systems: A normalized coprime factor approach , 1989 .

[9]  J.M.A. Scherpen,et al.  Multidomain modeling of nonlinear networks and systems , 2009, IEEE Control Systems.

[10]  J. Hoffmann,et al.  Normalized coprime factorizations in continuous and discrete time—a joint state-space approach , 1996 .

[11]  K. Glover,et al.  Robust stabilization of normalized coprime factor plant descriptions , 1990 .

[12]  Edmond A. Jonckheere,et al.  A new set of invariants for linear systems--Application to reduced order compensator design , 1983 .

[13]  Jacquelien M. A. Scherpen,et al.  Nonlinear input-normal realizations based on the differential eigenstructure of Hankel operators , 2005, IEEE Transactions on Automatic Control.

[14]  D. Mustafa,et al.  Controller reduction by H/sub infinity /-balanced truncation , 1991 .

[15]  P. Moylan,et al.  The stability of nonlinear dissipative systems , 1976 .

[16]  A. Antoulas,et al.  A Survey of Model Reduction by Balanced Truncation and Some New Results , 2004 .

[17]  Ha Binh Minh Model Reduction in a Behavioral Framework , 2009 .

[18]  P. Moylan Implications of passivity in a class of nonlinear systems , 1974 .

[19]  Jun-ichi Imura,et al.  Characterization of the strict bounded real condition of nonlinear systems , 1997, IEEE Trans. Autom. Control..

[20]  A. Isidori,et al.  Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems , 1991 .

[21]  Jacquelien M.A. Scherpen,et al.  Positive Real Balancing for Nonlinear Systems , 2007 .

[22]  J. M. A. Scherpen,et al.  Balancing for nonlinear systems , 1993 .

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

[24]  M. Ghribi,et al.  Robust optimal control of a DC motor , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[25]  J. Willems Dissipative dynamical systems part I: General theory , 1972 .

[26]  R. Ortega,et al.  On Transient Stabilization of Power Systems: A Power-Shaping Solution for Structure-Preserving Models , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[27]  J. Scherpen H∞ Balancing for Nonlinear Systems , 1996 .

[28]  A. Schaft L2-Gain and Passivity Techniques in Nonlinear Control. Lecture Notes in Control and Information Sciences 218 , 1996 .

[29]  D. Lukes Optimal Regulation of Nonlinear Dynamical Systems , 1969 .

[30]  Romeo Ortega,et al.  Interconnection and Damping Assignment Passivity-Based Control: A Survey , 2004, Eur. J. Control.

[31]  Jacquelien M. A. Scherpen,et al.  Passivity preserving model order reduction for the SMIB , 2008, 2008 47th IEEE Conference on Decision and Control.

[32]  U. Desai,et al.  A transformation approach to stochastic model reduction , 1984 .

[33]  K. Glover,et al.  Robust stabilization of normalized coprime factor plant descriptions with H/sub infinity /-bounded uncertainty , 1989 .

[34]  J. Willems The Generation of Lyapunov Functions for Input-Output Stable Systems , 1971 .

[35]  B. Moore Principal component analysis in linear systems: Controllability, observability, and model reduction , 1981 .

[36]  van der Arjan Schaft,et al.  Normalized coprime factorizations and balancing for unstable nonlinear systems , 1994 .

[37]  P. Moylan,et al.  Dissipative Dynamical Systems: Basic Input-Output and State Properties , 1980 .

[38]  Jacquelien M. A. Scherpen,et al.  Energy functions for dissipativity-based balancing of discrete-time nonlinear systems , 2006, Math. Control. Signals Syst..

[39]  E. Jonckheere,et al.  A contraction mapping preserving balanced reduction scheme and its infinity norm error bounds , 1988 .