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LPV control with observer and dynamic compensator
2001 European Control Conference (ECC), 2001In this paper we will present two techniques to design a dynamic output feedback controller for Linear Parameter Varying (LPV) systems with H 2 constraint. Assuming that the model is polytopic, the first technique allows a direct synthesis of a controller having the same LPV structure as the system.
Mohamed Hechmi Bouazizi +2 more
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A Model Decomposition Framework for LPV Systems
2018 IEEE Conference on Decision and Control (CDC), 2018The paper proposes a systematic framework for efficient decomposition of Linear Parameter Varying (LPV) systems. Our aim is to reveal the topological structure of the system, to facilitate various analysis and synthesis methods. For this purpose, first we extend the notion of Gramian based interaction measure for parameter dependent systems.
Tamás Luspay +3 more
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Stable controller interpolation for LPV systems
2008 American Control Conference, 2008This paper examines the gain-scheduling problem with a particular focus on controller interpolation with guaranteed nonlinear stability. For linear parameter varying model representations, a method of interpolating between controllers utilizing the Youla parameterization is proposed.
Young Joon Chang, Bryan P. Rasmussen
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LPV Equivalence Transformations
2010In this chapter, we continue the discussion of the LPV behavioral framework by establishing equivalence transformations between the state-space and the input-output representation domains. These equivalence transformations enable the comparison and analysis of LPV model structures and provide essential tools to formulate the identification approach of ...
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2014
The goal of this chapter is to introduce the main ways for representing linear parameter-varying systems and emphasize their ability to represent a wide class of dynamical systems. A classification of the types of parameters regarding their mathematical properties and their physical meaning is also given.
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The goal of this chapter is to introduce the main ways for representing linear parameter-varying systems and emphasize their ability to represent a wide class of dynamical systems. A classification of the types of parameters regarding their mathematical properties and their physical meaning is also given.
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Polytopic LPV approaches for intelligent automotive systems: State of the art and future challenges
Mechanical Systems and Signal Processing, 2021Hui Zhang, Haiping Du, Anh-Tu Nguyen
exaly
2014
This chapter first presents the main stability and instability results for general dynamical systems. These results are then further adapted to the analysis of linear parameter-varying systems in the generic, polytopic and LFT frameworks. Notably, the notions of quadratic and robust stability using quadratic Lyapunov functions are introduced.
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This chapter first presents the main stability and instability results for general dynamical systems. These results are then further adapted to the analysis of linear parameter-varying systems in the generic, polytopic and LFT frameworks. Notably, the notions of quadratic and robust stability using quadratic Lyapunov functions are introduced.
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2010
This chapter is devoted to the discretization of LPV systems through the discrete-time projection of their state-space representations. Both exact and approximative approaches are developed in a zero-order hold setting. Primary attention is given to the discretization of state-space representations with static coefficient dependence.
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This chapter is devoted to the discretization of LPV systems through the discrete-time projection of their state-space representations. Both exact and approximative approaches are developed in a zero-order hold setting. Primary attention is given to the discretization of state-space representations with static coefficient dependence.
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2014
This chapter is devoted to the development of control-laws for linear parameter-varying systems. The LPV framework allows for an easy design of gain-scheduled controllers, that is, controllers whose state-space structure depends on the value of the parameters thereby used as scheduling variables.
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This chapter is devoted to the development of control-laws for linear parameter-varying systems. The LPV framework allows for an easy design of gain-scheduled controllers, that is, controllers whose state-space structure depends on the value of the parameters thereby used as scheduling variables.
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2012
The framework of Linear Parameter Varying (LPV) systems concerns linear dynamical systems whose state-space representations depend on exogenous nonstationary parameters. Since its introduction by Shamma and Athans in 1988 to model gain-scheduling, the LPV paradigm has become a standard formalism in systems and controls, with many papers devoted to ...
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The framework of Linear Parameter Varying (LPV) systems concerns linear dynamical systems whose state-space representations depend on exogenous nonstationary parameters. Since its introduction by Shamma and Athans in 1988 to model gain-scheduling, the LPV paradigm has become a standard formalism in systems and controls, with many papers devoted to ...
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