Lyapunov methods

Part 1B | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

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KAUST

PDE Backstepping Boundary Control of Parabolic PDEs Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 1B

Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

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KAUST

Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly

Part 1A | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

-

KAUST

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

Part 2A | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

-

KAUST

Abstract Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. Generally, key aspects of these processes operating mode are driven by convection phenomena with a spatiotemporal dynamic that cannot be approximated straightforwardly using a finite

Part 2B | PDE Backstepping Boundary Control of Parabolic PDEs

Prof. Mamadou Diagne, Rensselaer Polytechnic Institute

-

KAUST

Partial Differential Equations (PDEs) are often used to model various complex physical systems. Representative engineering applications such as heat exchangers, transmission lines, oil wells, road traffic, multiphase flow, melting phenomena, supply chains, collective dynamics, and even chemical processes governing the state of charge of Lithium-ion battery, extrusion, reactors to mention a few. This course will explore the boundary control of a class of parabolic PDE via the well-known backstepping method.

State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2B

Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

-

KAUST

Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly

State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2C

Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

-

KAUST

Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly

State Estimation for Nonlinear Dynamical Systems: Methods and Applications | Part 2A

Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

-

KAUST

Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly

State estimation for nonlinear dynamical systems: Methods and applications | Part 1C

Prof. Ali Zemouche, Université de Lorraine and Khadidja Chaïb Draa, TechnOptiz

-

KAUST

Abstract In this lecture we will address the problem of observer design for nonlinear systems. It is mainly devoted to Linear Matrix Inequality (LMI) based methods and their use to improve observer design for a large class of nonlinear systems. A second part of this course will be dedicated to simple but useful contributions to high-gain observer design, where some techniques to improve performances of the standard high gain observer will be presented. Before tackling the problem of observer design, basic notions on stability by using direct and indirect Lyapunov methods will be thoroughly