Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

This chapter proposes a new control approach for the turbocharged air system of a gasoline engine. To simplify the control implementation task, static lookup tables (LUTs) of engine data are used to estimate the engine variables in place of complex dynamical observer and/or estimators. The nonlinear control design is based on the concept of robust feedback linearization which can account for the modeling uncertainty and the estimation errors induced by the use of engine lookup tables. The control feedback gain can be effectively computed from a convex optimization problem. Two control strategies have been investigated for this complex system: drivability optimization and fuel reduction. The effectiveness of the proposed control approach is clearly demonstrated with an advanced engine simulator

Convergence d'observateurs flous sous formes descripteurs : application à l'homme en station debout

International audienceUne classe d'observateurs non linéaires sous forme descripteurs basée sur une modélisation de type Takagi-Sugeno (TS) est décrite. Les conditions de convergence de l'erreur de reconstruction sont obtenues sous la forme de problèmes LMI. L'intérêt de ce type de représentation est, pour certains modèles non linéaires, de réduire la conservativité des résultats classiques. Cette réduction se fait au travers de la réduction du nombre de règles du modèle TS. Une application à la biomécanique de l'homme en station debout est proposée. Un observateur à entrées inconnues est défini afin d'estimer les vitesses et couples articulaires à partir de mesures de positions obtenues par un système optoélectronique de capture du mouvement

Trajectory Tracking Control Design for Large-Scale Linear Dynamical Systems With Applications to Soft Robotics

International audienceThis article presents new results to control process modeled through linear large-scale systems. Numerical methods are widely used to model physical systems, and the finite-element method is one of the most common methods. However, for this method to be precise, it requires a precise spatial mesh of the process. Large-scale dynamical systems arise from this spatial discretization. We propose a methodology to design an observer-based output feedback controller. First, a model reduction step is used to get a system of acceptable dimension. Based on this low-order system, two linear matrix inequality problems provide us, respectively, with the observer and controller gains. In both the cases, model and reduction errors are taken into account in the computations. This provides robustness with respect to the reduction step and guarantees the stability of the original large-scale system. Finally, the proposed method is applied to a physical setup-a soft robotics platform-to show its feasibility

Stabilisation non quadratique locale pour des modèles continus de type Takagi-Sugeno

International audienceLe papier traite de la stabilité non quadratique des modèles quasi-LPV ou dits de Takagi-Sugeno. Le problème des fonctions non quadratiques est de pouvoir prendre en compte la dérivée des fonctions non linéaires qui de façon générale dépendent de l'état. L'idée développée dans ce travail est de réduire la stabilisation globale-qui n'est pas toujours ni réaliste, ni réalisable pour les systèmes non linéaires-à une stabilisation locale. Dans ce contexte, on montre que l'obtention d'inégalités matricielles linéaires (LMI) peut-être fructueuse. Mots-clés-Inégalité Matricielle Linéaire (LMI), Fonctions de Lyapunov non quadratiques, Modèles Takagi-Sugeno

LPV Framework for Non-Linear Dynamic Control of Soft Robots using Finite Element Model

International audienceThis work presents a methodology to control soft robots using a reduced order nonlinear finite element model. The Linear Parameter-Varying (LPV) framework is used both to model the robot along a prescribed trajectory and to design its control law. Model reduction algorithms along with radial basis functions network are used to identify the nonlinear behavior of the robot. Finally, the method is validated through simulation experiments

Control Design for Soft Robots based on Reduced Order Model

International audienceInspired by nature, soft robots promise disruptive advances in robotics. Soft robots are naturally compliant and exhibit nonlinear behavior, which makes their study challenging. No unified framework exists to control these robots, especially when considering their dynamics. This work proposes a methodology to study this type of robots around a stable equilibrium point. It can make the robot converge faster and with reduced oscillations to a desired equilibrium state. Using computational mechanics, a large-scale dynamic model of the robot is obtained and model reduction algorithms enable the design of low order controller and observer. A real robot is used to demonstrate the interest of the results

Reduced Order Control of Soft Robots with Guaranteed Stability

International audienceThis work offers the ability to design a closed-loop strategy to control the dynamics of soft robots. A numericalmodel of a robot is obtained using the Finite Element Method,which leads to work with large-scale systems that are difficult tocontrol. The main contribution is a reduced order model-basedcontrol law, that consists in two main features: a reduced statefeedback tunes the performance while a Lyapunov functionguarantees the stability of the large-scale closed-loop systems.The method is generic and usable for any soft robot, as long asa FEM model is obtained. Simulation results show that we cancontrol and reduce the settling time of the soft robot and makeit converge faster without oscillations to a desired position

Dynamic Control of Soft Robots

International audienceSoft robots present several advantages. However, one of the main challenges of this new field of robotics is to control these robots. The methods used to control rigid robots are not directly relevant and new approaches have to be invented or updated to be applied to this kind of robots. This paper introduces control solutions for soft robots studies taking into account dynamics of the system