A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control

Abstract Gain-scheduled control based on linear parameter-varying (LPV) models derived from local linearizations is a widespread nonlinear technique for tracking time-varying setpoints. Recently, a nonlinear control scheme based on Control Contraction Metrics (CCMs) has been developed to track arbitrary admissible trajectories. This paper presents a comparison study of these two approaches. We show that the CCM based approach is an extended gain-scheduled control scheme which achieves global reference-independent stability and performance through an exact control realization which integrates a series of local LPV controllers on a particular path between the current and reference states.

[1]  Ian R. Manchester,et al.  Nonlinear stabilization via Control Contraction Metrics: A pseudospectral approach for computing geodesics , 2016, 2017 American Control Conference (ACC).

[2]  Sebastian Engell,et al.  Gain-scheduling trajectory control of a continuous stirred tank reactor , 1998 .

[3]  G. Scorletti,et al.  A theoretical framework for gain scheduling , 2003 .

[4]  A. Packard,et al.  Robust performance of linear parametrically varying systems using parametrically-dependent linear feedback , 1994 .

[5]  G. D'Mello,et al.  Taking robust LPV control into flight on the VAAC Harrier , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

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

[7]  Ian R. Manchester,et al.  An Amendment to "Control Contraction Metrics: Convex and Intrinsic Criteria for Nonlinear Feedback Design" , 2017, ArXiv.

[8]  Ian R. Manchester,et al.  Robust Control Contraction Metrics: A Convex Approach to Nonlinear State-Feedback ${H}^\infty$ Control , 2018, IEEE Control Systems Letters.

[9]  Christian Hoffmann,et al.  A Survey of Linear Parameter-Varying Control Applications Validated by Experiments or High-Fidelity Simulations , 2015, IEEE Transactions on Control Systems Technology.

[10]  Vincent Fromion,et al.  The weighted incremental norm approach: from linear to nonlinear Hinfinity control , 2001, Autom..

[11]  Pablo A. Parrilo,et al.  Semidefinite programming relaxations for semialgebraic problems , 2003, Math. Program..

[12]  John W. Simpson-Porco,et al.  Equilibrium-Independent Dissipativity With Quadratic Supply Rates , 2017, IEEE Transactions on Automatic Control.

[13]  Jean-Jacques E. Slotine,et al.  On Contraction Analysis for Non-linear Systems , 1998, Autom..

[14]  Ian R. Manchester,et al.  Distributed Economic MPC With Separable Control Contraction Metrics , 2017, IEEE Control Systems Letters.

[15]  A. A. Bachnas,et al.  A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study , 2014 .

[16]  Wilson J. Rugh,et al.  Analytical Framework for Gain Scheduling , 1990, 1990 American Control Conference.

[17]  I. Holopainen Riemannian Geometry , 1927, Nature.

[18]  Ian R. Manchester,et al.  Distributed Nonlinear Control Design Using Separable Control Contraction Metrics , 2018, IEEE Transactions on Control of Network Systems.

[19]  Roland Toth,et al.  Modeling and Identification of Linear Parameter-Varying Systems , 2010 .

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

[21]  Vincent Fromion,et al.  Toward nonlinear tracking and rejection using LPV control , 2015 .

[22]  Rodolphe Sepulchre,et al.  A Differential Lyapunov Framework for Contraction Analysis , 2012, IEEE Transactions on Automatic Control.