Empirical Computation of Reachability Gramian for Linear-Time Varying Systems

Abstract This paper deals with the approximate computation of the reachability gramian for linear time-varying(LTV) systems over a finite time interval. To this end, we use state trajectory information obtained by simulating the system for various inputs and initial conditions. We propose three techniques for computing the reachability gramian. The first two techniques use state variable data of the original LTV system realization while the third uses state variable data of the modified adjoint of the original LTV realization. We demonstrate the workability of these methods with the help of a numerical example. Similar to our work, Perev (2018); Perev (2019) deal with computing finite interval gramians of linear time-varying systems using system trajectory information. However, our methods avoid the computation of the state-transition matrix, which is essential in Perev’s work.

[1]  Jerrold E. Marsden,et al.  Empirical model reduction of controlled nonlinear systems , 1999, IFAC Proceedings Volumes.

[2]  Anders Rantzer,et al.  A novel approach to balanced truncation of nonlinear systems , 2009, 2009 European Control Conference (ECC).

[3]  J. D. Stigter,et al.  An Efficient Method to Assess Local Controllability and Observability for Non-Linear Systems , 2018 .

[4]  Yu Kawano,et al.  Empirical Differential Balancing for Nonlinear Systems , 2017 .

[5]  K. Perev Computation of system gramians for linear time-varying systems , 2018 .

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

[7]  Thomas F. Edgar,et al.  Balancing Approach to Minimal Realization and Model Reduction of Stable Nonlinear Systems , 2002 .

[8]  Thomas F. Edgar,et al.  An improved method for nonlinear model reduction using balancing of empirical gramians , 2002 .

[9]  Arthur J. Krener,et al.  Measures of unobservability , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[10]  T. Edgar,et al.  Controllability and observability covariance matrices for the analysis and order reduction of stable nonlinear systems , 2003 .

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

[12]  Marissa Condon,et al.  Empirical Balanced Truncation of Nonlinear Systems , 2004, J. Nonlinear Sci..

[13]  Geir E. Dullerud,et al.  Model reduction of periodic systems: a lifting approach , 2005, Autom..

[14]  J. Hahn,et al.  On the use of empirical gramians for controllability and observability analysis , 2005, Proceedings of the 2005, American Control Conference, 2005..

[15]  J. Peraire,et al.  Balanced Model Reduction via the Proper Orthogonal Decomposition , 2002 .

[16]  Clarence W. Rowley,et al.  Snapshot-Based Balanced Truncation for Linear Time-Periodic Systems , 2007, IEEE Transactions on Automatic Control.

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

[18]  Yu Kawano,et al.  Balanced Model Reduction for Linear Time-Varying Symmetric Systems , 2019, IEEE Transactions on Automatic Control.

[19]  Clarence W. Rowley,et al.  Model Reduction for fluids, Using Balanced Proper Orthogonal Decomposition , 2005, Int. J. Bifurc. Chaos.

[20]  Nathan D. Powel,et al.  Empirical observability Gramian rank condition for weak observability of nonlinear systems with control , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[21]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[22]  A Simple Method for Orthogonal Polynomial Approximation of Linear Time-Varying System Gramians , 2019, IFAC-PapersOnLine.