Thomas Chevet

ESIGELEC

Many repetitive control problems are characterized by the fact that disturbances have the same effect in each successive execution of the same control task. Such disturbances comprise the lumped representation of unmodeled parts of the open-loop system dynamics, a systematic model-mismatch or, more generally, deterministic yet unknown uncertainty....

Many repetitive control problems are characterized by the fact that disturbances have the same effect in each successive execution of the same control task. Such disturbances comprise the lumped representation of unmodeled parts of the open-loop system dynamics, a systematic model-mismatch or, more generally, deterministic yet unknown uncertainty....

The state estimation of repetitive processes with periodically repeated trajectories can be interpreted as the dual task of iterative learning control design. While the latter has been widely investigated over the last two decades, only few approaches exist for the design of iterative learning observers. However, the exploitation of the knowledge a...

The state estimation of repetitive processes with periodically repeated trajectories can be interpreted as the dual task of iterative learning control design. While the latter has been widely investigated over the last two decades, only few approaches exist for the design of iterative learning observers. However, the exploitation of the knowledge a...

This letter proposes a novel robust interval observer for a two-dimensional (treated as a synonym for a double-indexed system) linear time-invariant discrete-time system described by the Fornasini-Marchesini second model. This system is subject to unknown but bounded state disturbances and measurement noise. Built on recent interval estimation stra...

This paper proposes a new interval observer for joint estimation of the state and unknown inputs of a discrete-time linear parameter-varying (LPV) system with an unmeasurable parameter vector. This system is assumed to be subject to unknown inputs and unknown but bounded disturbances and measurement noise, while the parameter-varying matrices are e...

This letter proposes an unknown input zonotopic Kalman filter-based interval observer for discrete-time linear time-invariant systems. In such contexts, a change of coordinates decoupling the state and the unknown inputs is often used. Here, the dynamics are rewritten into a discrete-time linear time-invariant descriptor system by augmenting the st...

This paper proposes a new interval observer for continuous-time linear parameter-varying systems with an unmeasurable parameter vector subject to unknown but bounded disturbances. The parameter-varying matrices are assumed to be elementwise bounded. This observer is used to compute a so-called residual interval used for sensor fault detection by ch...

In the context of state estimation of dynamical systems subject to bounded perturbations and measurement noises, this paper proposes an application of a guaranteed ellipsoidal-based set-membership state estimation technique to estimate the linear position of an octorotor used for radar applications. The size of the ellipsoidal set containing the re...

This thesis presents Model Predictive Control (MPC) techniques for the deployment and the reconfiguration of a dynamical Multi-Agent System (MAS) in a bounded convex two-dimensional area. A novel decentralized predictive control law for the Voronoi-based deployment of a fleet of quadrotor Unmanned Aerial Vehicles (UAVs) is derived. The proposed dec...

(Demonstrator late breaking results paper)

This paper presents a new decentralized algorithm for the deployment and reconfiguration of a multi-agent formation in a convex bounded polygonal area when considering several outgoing agents. The system is deployed over a two-dimensional convex bounded area, each agent being driven by its own linear model predictive controller. At each time instan...

This paper proposes a new chance-constrained model predictive control (CCMPC) algorithm with state estimation applied to the two-dimensional deployment of a multi-vehicle system where each agent is subject to process noise and measurement noise. The bounded convex area of deployment is partitioned into time-varying Voronoi cells defined by the posi...

In the context of innovative control laboratories, this paper presents a new engineering course applying basic automatic control and optimization concepts to the cooperative control of Unmanned Aerial Vehicles (UAVs) formations. Innovatory methods of active learning such as Problem-Based Learning (PBL) in small tutored groups are proposed, as well...

This paper presents a decentralized Voronoi-based linear model predictive control (MPC) technique for the deployment and reconfiguration of a multi-agent system composed of unmanned aerial vehicles (UAVs) in a bounded area. At each time instant, this area is partitioned into non-overlapping time-varying Voronoi cells associated to each UAV agent. T...

This paper focuses on the design of a linear Kalman filter and an extended Kalman filter for the estimation of an octorotor unmanned aerial vehicle’s (UAV) state in the context of Synthetic Aperture Radar image reconstruction. A comparison to a linear interpolation method is also proposed. The Kalman filters are developed based on a complete nonlin...