Nonlinear balanced realizations

Properties of sliding interval balancing for linear systems are revisited, as this notion is basic for our extension of balanced realizations to nonlinear systems. Nonlinear balancing is based upon three principles: 1) Balancing should be defined with respect to a nominal flow; 2) Only gramians defined over small time intervals should be used to preserve the accuracy of the linear perturbation model and; 3) Linearization should commute with balancing, in the sense that the linearization of a globally balanced model should correspond to the balanced linearized model in the original coordinates. Whereas it is generically possible to define a balanced framework locally, it is not possible to do so globally, and the notion of balancing was therefore relaxed, using integrating factors, to that of uncorrelatedness. As obstruction to the integrability of the (scaled) Jacobian is generic in dimensions n > 2, we focus our attention on the planar case, for which global balanced or uncorrelated realizations exist As with linear systems, the metric provided by a canonical gramian is shown to provide useful information about the dynamics of the system and the topology of the state space.

[1]  Erik I. Verriest Pseudo Balancing for Discrete Nonlinear Systems , 2022 .

[2]  Discrete Time Nonlinear Balancing , 2001 .

[3]  Erik I. Verriest,et al.  Algebraic theory for time variant linear systems: modes, minimality and reachability and observability of interconnected systems , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[4]  Jer-Nan Juang,et al.  Model reduction in limited time and frequency intervals , 1990 .

[5]  T. Kailath,et al.  On generalized balanced realizations , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[6]  Serkan Gugercin,et al.  A time-limited balanced reduction method , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[7]  Erik I. Verriest On balanced realizations for time variant linear systems , 1980 .

[8]  P. Olver Equivalence, Invariants, and Symmetry: References , 1995 .

[9]  J. Marsden,et al.  A subspace approach to balanced truncation for model reduction of nonlinear control systems , 2002 .

[10]  Nonlinear balancing and Mayer-Lie interpolation , 2004, Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the.

[11]  Erik I. Verriest,et al.  FLOW BALANCING NONLINEAR SYSTEMS , 2000 .

[12]  W. Steven Gray,et al.  Controllability and Observability Functions for Model Reduction of Nonlinear Systems , 1996 .

[13]  Balancing nonlinear systems near attracting in variant manifolds , 1999, 1999 European Control Conference (ECC).

[14]  Constantin Carathéodory,et al.  Calculus of variations and partial differential equations of the first order , 1965 .

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

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

[17]  Perinkulam S. Krishnaprasad,et al.  Computation for nonlinear balancing , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).