Abstract

International audience; This paper deals with sensors faults detection and isolation using method by observers. The principle is to reconstruct the state vector and measurements of the system by sliding mode observers and compare the estimated outputs with those measured. In this work, a multi observer's technique is used. It consists on construction of many observers, at least one observer for each output (sensor measurement). Each observer must be robust to noises and to other uncertainties but sensitive to sensor faults. The residual is the estimation error witch is the difference between sensor measurement and its estimated. Without failure in the sensor, this residual remains around zero and if a fault is occurred, it deviates significantly from zero. This method is applied to an autonomous electric vehicle called RobuCar. Simulation results are given at the end to show the effectiveness of the approach.

Abstract

International audience; The autonomous navigation of an electric vehicle requires the implementation of a number of sensors and actuators intended to inform it about his environment or his position and velocity and deliver necessary inputs. That's why it is important to detect and locate sensor and actuator faults as soon as possible to enable the operator to run the vehicle in degraded mode or use the fault tolerant control system if it exists. The main purpose of this paper deals with sensors or actuators faults diagnosis of autonomous vehicle. A diagnosis method using a nonlinear model of the vehicle is developed. Nonlinear state space model of the autonomous electric vehicle is used with the method of nonlinear analytical redundancy to detect and to isolate faults occurred on sensors or actuators. Computer simulations are carried out to verify the effectiveness of the method.