Lynda Seddiki

Actuator and sensor fault detection and isolation of an actuated seat via nonlinear multi-observers

In this paper, a model-based approach to detect and to isolate sensors and actuators faults using nonlinear sliding mode observers is proposed for an actuated seat. The goal is to ensure the comfort and the security of the users in simulators applications. The principle is to reconstruct the state vector of the system by sliding mode observers and to compare the estimated outputs with those measured as residuals. In this work, a multi-observers technique is used. It consists of designing multiple observers such that each observer must be robust to noises and to other uncertainties but sensitive to each actuator or sensor fault. Simulation results are given to show the effectiveness of the approach.

A Fault Detection Approach for Robotic Systems Combining the Data Obtained from Sensor Measurements and Linear Observer-Based Estimations

The design of a fault detection device represents one of the major challenges that manufacturers of robotic systems face today. The detection process requires the use of a number of sensors to monitor the operation of these systems. However, the implementation costs and constraints of these sensors often lead designers to optimize the number used. This could accordingly induce a lack of necessary measures for the optimal detection of failures. One way to bridge this gap consists of realizing model-based estimations of non-measurable state variables describing the dynamics of the real system. This paper presents an approach based on mixed data (measured data and estimated data) for the detection of faults in robotic systems. The proposed fault detection approach is performed using a decision tree classifier. The data used to build this learning stage are obtained from the available measurements of the real system, according to its standard actions. Then, to improve the database classification with unmeasurable data, a linear observer is designed from an analytical model. From the estimations provided by the linear observer, new attributes are built, with the aim of enriching the knowledge used by the classifier and thus improving the rate of fault detection. Finally, an experiment on a robotized actuated seat is presented to illustrate the proposed combined linear observer and classifier approach.

Merging fuzzy observer-based state estimation and database classification for fault detection and diagnosis of an actuated seat

This paper proposes an approach for Fault Detection and Diagnosis (FDD) of an actuation system for passengers seats in commercial aircrafts. The FDD is performed using classification algorithms. The supervised classification algorithms are usually based on data collected from the different sensors installed on a real system. Thus, to reduce the number of embedded sensors and so the costs of seat components in commercial aircrafts, a fuzzy Takagi-Sugeno (T-S) state observer is considered to estimate non-measured state variables in order to enrich the database used for the supervised classification process. From experimental measurements on a prototype of the actuated seat, the benefit of adding T-S observer-based estimations is illustrated through a comparison of the classification results obtained using databases without then with estimated data.

Diagnosis of an actuated seat in a constrained space via Takagi-Sugeno sliding mode observers

This paper deals with actuator faults detection and isolation for an actuated seat described by Takagi-Sugeno multiple models. The goal is to ensure the comfort and the security of the users in simulator applications. Sliding mode observers based on T-S models are designed to estimate the system state vector. Residuals are generated by the comparison of measured and estimated outputs. In this work, a multi-observers technique is used. It consists of the construction of many observers such that each observer must be robust to noises and to other uncertainties but sensitive to one actuator fault. Simultaneous faults occurring on the actuated seat can be detected and isolated using this method. Simulation results are given to show the effectiveness of this approach.

Design of a trajectory generator for a lower-limbs rehabilitation device

Abstract This paper deals with the design of a trajectory generator for Sys-Reeduc, a lower-limb rehabilitation device in Closed Muscular Chain (CMC). The goal is to generate, from the measurement of the patients voluntary efforts applied to the device, the trajectories to be tracked by the rehabilitation device with accordance to usual rehabilitation protocols such as isokinetic, isometric or isotonic movements. Therefore, rehabilitation trajectories are split into elementary movements (static, isokinetic extension, etc.) which are then combined through discrete states machines and activated from convenient thresholds crossings of the users efforts. This allows the user to drive the device at his own initiative and make safe the release of rehabilitation protocols on such apparatus.