Dominique Beauvois

A mixed extended-unscented filter for attitude estimation with magnetometer sensor

A priori information given by the complete modelling of the ballistic behavior (trajectory, attitude) of a projectile is simplified to give a pertinent reduced evolution model. A new mixed Kalman filter based on Extended and unscented Kalman filter is designed to estimate 3 attitude angles from measures of the magnetic field of the Earth given by a three-axis magnetometer sensor embedded on the projectile. The algorithm has been tested in simulation, using realistic evolution of attitude data for a shot with a 155 mm rotating projectile over a distance of 16 km, with wind and measurement noise. The results show that we can estimate milliradians with non-linear equations and approximations, with good accuracy

Cooperative Nonlinear Model Predictive Control for Flocks of Vehicles

Abstract This paper describes the guidance of a group of autonomous cooperating vehicles using model predictive control. The developed control strategy allows to find a feasible near optimal control sequence with a short and constant computation delay in all situations. It makes use of the nonlinear model of the vehicle and takes other vehicle intentions into account. Numerical simulations are provided where a group of vehicles must reach several way-points while avoiding obstacles and collisions inside the group. These simulations allow to compare computation delay and efficiency of the proposed approach with traditional optimisation.

Stability of a nonlinear Moving Horizon Estimator with pre-estimation

In this paper, a Moving Horizon Estimator with pre-estimation (MHE-PE) is proposed for discrete-time nonlinear systems under bounded noise. While the classical Moving Horizon Estimator (MHE) compensates for model errors by estimating the process noise sequence over the horizon via optimization, the MHE-PE does it using an auxiliary estimator. The MHE-PE is shown to require significantly less computation time compared to the MHE, while providing the same order of magnitude of estimation errors. The stability of the estimation errors of the MHE-PE is also proven and an upper bound on its estimation errors is derived. Performances of the MHE-PE is illustrated via a simulation example of pressure estimation in a gas-phase reversible reaction.

Robustness analysis of a Moving Horizon Estimator for space debris tracking during atmospheric reentry

Trajectory estimation during atmospheric reentry of ballistic objects such as space debris is a very complex problem due to high variations of their ballistic coefficients. In general, the characteristics of the tracked object are not accurately known and an assumption on the dynamics of the ballistic coefficient has to be made in the estimation model. The designed estimator must hence prove to be robust enough to such model uncertainties, and to bad initialization if no good prior information on the initial position, velocity, and the characteristics of the object is available. Robustness of a Moving Horizon Estimator (MHE) is studied in this paper and compared to several other filters: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Regularized Particle Filter (RPF). The performances of the filters are analysed in terms of convergence percentage, accuracy, robustness to bad initialization, and computation time, via Monte Carlo simulations of trajectories of several space debris. Contrary to the classical tracking problem of supersonic ballistic objects for which RPF has been proven to be efficient in the literature, it is shown that its performance are overcome by MHE for the space debris tracking problem considered in this paper.

Space debris trajectory estimation during atmospheric reentry using moving horizon estimator

Space debris trajectory estimation during atmospheric reentry is a complex problem. For such an object the ballistic coefficient, which characterizes the response of the object to aerodynamics braking, is usually a highly nonlinear function of time. This function may be unknown if no a priori information on the object type is available. It is therefore interesting to design a robust estimator that would provide accurate estimates of the state of the tracked object, from available measurements. In this paper, a Moving Horizon Estimator (MHE) is implemented for trajectory estimation of a space debris during atmospheric reentry, from radar measurements. Its performances in terms of convergence and accuracy are analysed and compared with that of an Extended Kalman Filter (EKF), traditionally applied to this type of problem.

Guidance of Flocks of Vehicles Using Virtual Signposts

Abstract In this paper, we present how virtual signposts can be used to guide a flock of autonomous vehicles along a desired path and to a desired location. Signposts can also be used to make the flock avoid obstacles with the interesting feature of indicating the best direction. These virtual signposts allow to define specific desired path without virtual leader constraints. Simply reaching a final location is also possible and both approaches can be mixed if necessary. Stranded vehicles can be put back on the track with carefully placed signposts.

Projectile attitude and position determination using magnetometer sensor only

A priori information given by the complete modelling of the ballistic behavior (trajectory, attitude) of the projectile is simplified to give a pertinent reduced evolution model. An algorithm based on extended Kalman filters is designed to determinate: • position: x,y,z references in earth frame. • value and direction of the velocity vector; its direction is given by 2 angles (η and θ). • attitude around velocity vector given by 3 angles: roll angle in the range [0, 2π], angle of attack α and side-slip angle β in the range of few milliradians. The estimation is based on the measures of the magnetic field of the earth given by a three-axis magnetometer sensor embedded on the projectile. The algorithm also needs the knowledge of the direction of the earth magnetic fields in the earth frame and aerodynamics coefficients of the projectile. The algorithm has been tested on simulation, using real evolution of attitude data for a shot with a 155 mm rotating projectile over a distance of 16 km, with wind and measurement noise. The results show that we can estimate milliradians with non-linear equations and approximations, with good precision.

Moving Horizon Estimation with Pre-Estimation (MHE-PE) for 3D space debris tracking during atmospheric re-entry

Space debris tracking during atmospheric re-entry is a very complex problem due to high variations with time of the ballistic coefficient. The nature of these variations is generally unknown and an assumption has to be made in the estimation model which can result in high model errors. An estimator which is robust against model errors is therefore required. In previous work done by the authors, Moving Horizon Estimation (MHE) has been shown to outperform other classical nonlinear estimators in terms of accuracy and robustness against poor initialization for a simplified 1D case of space debris tracking during the re-entry. However, the large computation time of the MHE prevents its implementation for the 3D cases. Recently, the Moving Horizon Estimation with Pre-Estimation (MHE-PE) which requires much less computation time than the classical MHE while keeping its accuracy and robustness has been proposed. This paper therefore implements the MHE-PE to solve the 3D space debris tracking problem during the re-entry. Its performances are compared to some classical nonlinear estimators in terms of non-divergence percentage, accuracy and computation time through Monte Carlo simulations.

Modeling and Analysis of a Turbojet Fuel System

In aeronautics, two of the main key design drivers are the reduction of the fuel consumption and the reduction of its environmental impact. These two considerations are taken into account by both aircraft and engine manufacturers. On the engine regulation system level, the first objective is reached by reducing its mass. The second one can be achieved with a more advanced fuel system. This paper deals with the control of a new fuel system of a turbojet. In comparison with classical fuel systems, a hydraulic equipment of the original system has been removed to reduce the mass, which means that standard control of the turbojet is no more effective. After modeling the new system, its couplings are studied and a controller is designed. This paper is organized as follows: Section II introduces the system and its functioning; the dynamic model is described in Section III and Section IV presents the design of the control system and its performances. Section V compares different methods of simulation, in order to find the most effective one for the system. At last, conclusions are presented in Section VI.

A predictive multivariable helicopter robust design

The characteristic decomposition methodology and its use for the multivariable characteristic GPC (CGPC) are reviewed. The approach is illustrated via a helicopter application. The characteristic decomposition results and the simulations obtained by the MIMO CGPC are given. These simulations show that the initial feedback configuration has to be slightly modified to ensure the required performance/robustness. With good eigenstructure approximations, the CGPC design offers important advantages: (i) by means of eigen projection, it decouples and simplifies the prediction equations; (ii) by the ease of use of independent prediction parameters for each subsystem, it makes it possible to shape the individual subsystems behavior and, hence, the behavior of the overall system according to the necessary and sufficient generalized Nyquist condition; and (iii) the appropriate use of a matrix polynomial filter makes it possible to achieve the desired robustness.<<ETX>>