Optimisation-based modelling of LPV systems using an -objective

A method to identify linear parameter varying models through minimisation of an -norm objective is presented. The method uses a direct nonlinear programming approach to a non-convex problem. The reason to use -norm is twofold. To begin with, it is a well-known and widely used system norm, and second, the cost functions described in this paper become differentiable when using the -norm. This enables us to have a measure of first-order optimality and to use standard quasi-Newton solvers to solve the problem. The specific structure of the problem is utilised in great detail to compute cost functions and gradients efficiently. Additionally, a regularised version of the method, which also has a nice computational structure, is presented. The regularised version is shown to have an interesting interpretation with connections to worst-case approaches.

[1]  Laurent El Ghaoui,et al.  Robust Solutions to Least-Squares Problems with Uncertain Data , 1997, SIAM J. Matrix Anal. Appl..

[2]  R. Ravikanth,et al.  Identification of linear parametrically varying systems , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[3]  R. van de Molengraft,et al.  Experimental modelling and LPV control of a motion system , 2003, Proceedings of the 2003 American Control Conference, 2003..

[4]  Javad Mohammadpour,et al.  Control of linear parameter varying systems with applications , 2012 .

[5]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[6]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[7]  Andras Varga,et al.  Automated generation of LFT-based parametric uncertainty descriptions from generic aircraft models , 1998 .

[8]  D. Wilson Optimum solution of model-reduction problem , 1970 .

[9]  Venkataramanan Balakrishnan,et al.  System identification: theory for the user (second edition): Lennart Ljung; Prentice-Hall, Englewood Cliffs, NJ, 1999, ISBN 0-13-656695-2 , 2002, Autom..

[10]  Marco Lovera,et al.  Guest Editorial Special Issue on Applied LPV Modeling and Identification , 2011 .

[11]  P.M.J. Van den Hof,et al.  Modeling and Identification of Linear Parameter-Varying Systems, an Orthonormal Basis Function Approach , 2004 .

[12]  R. Th Modeling and identification of linear parameter-varying systems: an orthonormal basis function Approach , 2008 .

[13]  Andrea Bianco,et al.  Guest editorial for the special issue , 2009, Comput. Networks.

[14]  Harald Pfifer,et al.  Generation of Optimal Linear Parametric Models for LFT-Based Robust Stability Analysis and Control Design , 2011, IEEE Transactions on Control Systems Technology.

[15]  Mi-Ching Tsai,et al.  Robust and Optimal Control , 2014 .

[16]  M. Athans,et al.  Gain Scheduling: Potential Hazards and Possible Remedies , 1992, 1991 American Control Conference.

[17]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[18]  Daniel Petersson Nonlinear optimization approaches to H2-norm based LPV modelling and control , 2010 .

[19]  William Leithead,et al.  Survey of gain-scheduling analysis and design , 2000 .

[20]  Bassam Bamieh,et al.  Identification of linear parameter varying models , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[21]  Okko H. Bosgra,et al.  LPV control for a wafer stage: beyond the theoretical solution , 2005 .

[22]  P. Heuberger,et al.  Discrete time LPV I/O and state space representations, differences of behavior and pitfalls of interpolation , 2007, 2007 European Control Conference (ECC).

[23]  Arkadi Nemirovski,et al.  Robust optimization – methodology and applications , 2002, Math. Program..

[24]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[25]  Lawton H. Lee,et al.  Identification of Linear Parameter-Varying Systems Using Nonlinear Programming , 1999 .

[26]  G. Balas,et al.  Development of linear-parameter-varying models for aircraft , 2004 .

[27]  K. Poolla,et al.  Identification of linear parameter-varying systems via LFTs , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[28]  Wilson J. Rugh,et al.  Research on gain scheduling , 2000, Autom..

[29]  Jan Swevers,et al.  Interpolation-Based Modeling of MIMO LPV Systems , 2011, IEEE Transactions on Control Systems Technology.

[30]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[31]  M. Lovera,et al.  Identification for gain-scheduling: a balanced subspace approach , 2007, 2007 American Control Conference.

[32]  Jan Swevers,et al.  Interpolated Modeling of LPV Systems Based on Observability and Controllability , 2012 .

[33]  Michel Verhaegen,et al.  Subspace identification of MIMO LPV systems using a periodic scheduling sequence , 2007, Autom..

[34]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[35]  Charles Poussot-Vassal,et al.  Generation of a reduced-order LPV/LFT model from a set of large-scale MIMO LTI flexible aircraft models , 2012 .

[36]  A. Rantzer,et al.  System analysis via integral quadratic constraints , 1997, IEEE Trans. Autom. Control..