On the estimation of the equilibrium points of uncertain nonlinear systems

SUMMARY The analysis and control of nonlinear systems often require information about the location of their equilibrium points. This paper addresses the problem of estimating the set of equilibrium points of uncertain nonlinear systems, in particular, systems whose dynamics are described by a nonlinear function of the state depending polynomially on an uncertainty vector constrained in a polytope. It is shown that estimates of this set can be obtained by solving LMI problems, which are built through sum of squares techniques by introducing worst-case truncations of the nonlinearities and by exploiting homogeneity of equivalent representations. In particular, the computation of estimates with fixed shape and the problem of establishing their tightness is firstly considered. Then, the paper shows how this methodology can be used to address the computation of the minimum volume estimate and the construction of the smallest convex estimate. Examples with random and real systems illustrate the proposed methodology. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  Jean B. Lasserre,et al.  Global Optimization with Polynomials and the Problem of Moments , 2000, SIAM J. Optim..

[2]  Frank Allgöwer,et al.  Guaranteed steady state bounds for uncertain (bio-)chemical processes using infeasibility certificates , 2010 .

[3]  Jean B. Lasserre Robust global optimization with polynomials , 2006, Math. Program..

[4]  Frank Allgöwer,et al.  Guaranteed steady-state bounds for uncertain chemical processes , 2009 .

[5]  Etienne de Klerk,et al.  Exploiting special structure in semidefinite programming: A survey of theory and applications , 2010, Eur. J. Oper. Res..

[6]  M. Laurent Sums of Squares, Moment Matrices and Optimization Over Polynomials , 2009 .

[7]  Graziano Chesi,et al.  LMI Techniques for Optimization Over Polynomials in Control: A Survey , 2010, IEEE Transactions on Automatic Control.

[8]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[9]  B. Sturmfels SOLVING SYSTEMS OF POLYNOMIAL EQUATIONS , 2002 .

[10]  James M. Ortega,et al.  Iterative solution of nonlinear equations in several variables , 2014, Computer science and applied mathematics.

[11]  Leon O. Chua,et al.  Linear and nonlinear circuits , 1987 .

[12]  Dinesh Manocha,et al.  SOLVING SYSTEMS OF POLYNOMIAL EQUATIONS , 2002 .

[13]  B. Reznick Extremal PSD forms with few terms , 1978 .

[14]  G. Stengle A nullstellensatz and a positivstellensatz in semialgebraic geometry , 1974 .

[15]  E. Allgower,et al.  Computational Solution of Nonlinear Systems of Equations , 1990 .

[16]  A. Magnani,et al.  Tractable fitting with convex polynomials via sum-of-squares , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[17]  O. Taussky Sums of Squares , 1970 .

[18]  G. Chesi Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems , 2009 .

[19]  Graziano Chesi,et al.  Characterizing the solution set of polynomial systems in terms of homogeneous forms: an LMI approach , 2003 .

[20]  Graziano Chesi On the admissible equilibrium points of nonlinear dynamical systems affected by parametric uncertainty: Characterization via LMIs , 2010, 2010 IEEE International Symposium on Computer-Aided Control System Design.

[21]  P. Parrilo Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization , 2000 .

[22]  Masakazu Muramatsu,et al.  Sums of Squares and Semidefinite Programming Relaxations for Polynomial Optimization Problems with Structured Sparsity , 2004 .

[23]  Graziano Chesi,et al.  An LMI-based approach for characterizing the solution set of polynomial systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).