International audience

This paper considers the problem of fault-tolerance in two wheeled mobile robots (2WMR), using a robust sliding mode controller (SMC) coupled with a fault detection and exclusion (FDE) method. SMC can handle external matched disturbances, unmodeled dynamics and partial loss of effectiveness of actuators. However, it can not deal with erroneous sensor measurements and total actuator faults. For this purpose, a multiple model-based FDE method based on an informational framework is used, in order to detect and isolate faulty sensors and actuators. The information form of the Kalman filter (IF) is used for state estimation. The proposed IF uses two prediction models: a mathematical model with SM control signals as inputs, and an odometric model that takes encoders measurements as inputs. Residuals are generated using the Kullback-Leibler (KL) divergence, which computes the dissimilarity between the probability distributions given by the predicted estimations and sensors measurements. The proposed strategy excludes erroneous sensors measurements from the estimation process, which makes the proposed SMC algorithm law tolerant to these faulty sensors. Total Actuator faults, are also detected and isolated, but since the controllability of the system is not preserved, the 2WMR is stopped and an alarm is generated along with the position of the robot. A real-time experimentation on a real differential mobile robot is performed to validate the proposed strategy.

This paper presents a strategy to combine a fault tolerant multi-sensor data fusion with a closed loop controller scheme robust against external disturbances applied to a multi-robot mobile system tracking different trajectories. Multi-sensor fusion is ensured using an information filter which is the canonical form of the Kalman filter. Fault detection and exclusion strategy is proposed to eliminate any erroneous measurements, for this purpose, risiduals are generated using the Kullback-Leibler diveregence that compares the priori and posteriori distributions of the predicted and the corrected estimations respectively. Prediction model is based on odometry, with encoders data as input. Observation model is based on extra sensors observations. To optimise detections, an adaptive thresholding method based on the Kullback-Leibler Criteron is proposed. Trajectory tracking is achieved using a sliding mode controller (SMC), developped by inverting the dynamical model of mobile robots. SMC is robust against external matched disturbances, parameters variation and actuators detoriation, however it can not handle total loss of effectiveness, which makes detection and isolation of faulty actuators compulsory. For this purpose, controllers inputs are converted into expected elementary velocities and compared to real data obtained from fault tolerant multi-sensor data fusion. The main contribution of this paper is to combine a fault detection and exclusion (FDE) scheme with an enhanced sliding mode controller in order to detect and isolate both sensors and actuators faults. The method is applied to a multi-robot system. The obtained experimental results demonstrate the egibility and the effectiveness of the proposed approach.

In this paper, the problem of fault detection filter design for a class of nonlinear switched systems is investigated. First, the nonlinear switched system is transferred into a Takagi-Sugeno fuzzy switched model by using fuzzy if-then rules. Then, based on the persistent dwell-time switching signal and the quasi-time-dependent Lyapunov function technique, an efficient condition of the performance analysis result is obtained. Based on this, a fuzzy-parameter-dependent fault detection filter is designed such that the resulting error system is globally uniformly asymptotically stable with a nonweighted H∞performance index. Finally, simulation results are provided to show the effectiveness of the proposed technique.

This short paper develops an adaptive compensation control scheme for two physically linked two-wheel-drive mobile robots with multiple actuator faults. Kinematic and dynamic models are first proposed. Then, an adaptive control scheme is designed, which ensures system stability and asymptotic tracking properties. Simulation results verify its effectiveness.

The problem of fault-tolerant dynamic surface control (DSC) for a class of uncertain nonlinear systems with actuator faults is discussed and an active fault-tolerant control (FTC) scheme is proposed. Using the DSC technique, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, an accommodation scheme is proposed to compensate for both actuator time-varying gain and bias faults, and avoids the controller singularity. In addition, the proposed controller guarantees that all signals of the closed-loop system are semiglobally uniformly ultimately bounded, and converge to a small neighborhood of the origin. Finally, the effectiveness of the proposed FTC approach is demonstrated on a simulated aircraft longitudinal dynamics example.

This paper addresses the problem of fault-tolerant control (FTC) for near-space vehicle (NSV) attitude dynamics with actuator faults, which is described by a Takagi-Sugeno (T-S) fuzzy model. First, a general actuator fault model that integrated varying bias and gain faults, which are assumed to be dependent on the system state, is proposed. Then, sliding mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, for the two cases where the state is available or not, two accommodation schemes are proposed to compensate for the effect of the faults. These schemes do not need the condition that the bounds of the time derivative of the faults should be known. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of NSV are presented to demonstrate the efficiency of the proposed FTC approach.

This paper addresses the problem of fault-tolerant control for Takagi-Sugeno (T-S) fuzzy systems with actuator faults. First, a general actuator fault model is proposed, which integrates time-varying bias faults and time-varying gain faults. Then, sliding-mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault-diagnostic algorithm is proposed to estimate the actuator fault, which removes the classical assumption that the time derivative of the output errors should be known as in some existing work. Further, a novel fault-estimation observer is designed. Utilizing the estimated actuator fault, an accommodation scheme is proposed to compensate for the effect of the fault. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of a near-space hypersonic vehicle are presented to demonstrate the efficiency of the proposed approach.

This paper investigates the path-tracking problem for four-wheel-steering and four-wheel-driving electric vehicles with input constraints, actuator faults, and external resistance. A hybrid fault-tolerant control approach, which combines the linear-quadratic control method and the control Lyapunov function technique, is proposed. It not only maintains the vehicle's tracking performance in spite of faults, input constraints, and external resistance but also reduces the cost of the fault-tolerant process. A prototype vehicle from the Laboratoire d'Automatique, Ge¿nie Informatique et Signal (LAGIS), is particularly focused on illustrating the applicability of our approach.

Designing a dependable control system requires accurate methods to evaluate efficiently the dependability level of one given component architecture. This evaluation is crucial to determine the risks associated with system failures, and the remaining properties after fault occurrences. The dependability level of a control system depends not only on the kind of component failures that may occur, but also on the ordered sequences of the failure appearance. Classical evaluation methods, i.e. fault trees or failure mode and effect analysis, are not appropriate to handle these sequences. Our paper contributes on this aspect, and proposes a complete design methodology for dependable systems. This methodology uses ordered sequences of multiple failures to evaluate accurately the dependability level of all possible system's equipment architectures. Starting with the hierarchical functional decomposition of the system, the first step is to identify the dreaded events. Thus, the faulty behaviors of all possible system architectures are characterized with temporal operators. The set of system's operational architectures is finally determined by solving an optimization problem that considers both dependability objectives, and cost constraints. This methodology is applied to design a fire detection system for a railroad transportation system. In this paper, a complete methodology to design dependable control systems is presented. The innovative feature of this methodology is that it attempts to take into account time ordered sequences of failures. A new representation, called improved multi-fault tree, is defined. This tool allows us first to model failure relationships between functions, and second to evaluate the dependability level of a set of equipment architectures by the use of time ordered sequences of failures. Our design method provides a set of optimal architectures with given costs, and dependability levels. The designer can choose among these solutions trading among the costs, and dependability level specifications. The comparison between the new approach and the classical dependability method shows that the set of solutions for the multi-fault tree is smaller than the set of solutions for the classical one. The set is smaller, but the solutions are better because the new approach integrates temporal functions, and evaluates more precisely the level of dependability than with the traditional one.