Application of Radial Basis Function Network in Sensor Failure Detection

Aim To detect sensor failure in control system using a single sensor signal. Methods A neural predictor was designed based on a radial basis function network(RBFN), and the neural predictor learned the sensor signal on line with a hybrid algorithm composed of n means clustering and Kalman filter and then gave the estimation of the sensor signal at the next step. If the difference between the estimation and the actural values of the sensor signal exceeded a threshold, the sensor could be declared to have a failure. The choice of the failure detection threshold depends on the noise variance and the possible prediction error of neural predictor. Results and Conclusion\ The computer simulation results show the proposed method can detect sensor failure correctly for a gyro in an automotive engine.

Application of Wavelets Transform to Fault Detection in Rotorcraft UAV Sensor Failure

This paper describes a novel wavelet-based approach to the detection of abrupt fault of Rotorcrafi Unmanned Aerial Vehicle （RUAV） sensor system. By use of wavelet transforms that accurately localize the characteristics of a signal both in the time and frequency domains, the occurring instants of abnormal status of a sensor in the output signal can be identified by the multi-scale representation of the signal. Once the instants are detected, the distribution differences of the signal energy on all decomposed wavelet scales of the signal before and after the instants are used to claim and classify the sensor faults.

Finite Frequency Fuzzy H∞Control for Uncertain Active Suspension Systems With Sensor Failure

This paper investigates the problem of finite frequency fuzzy H_∞ control for uncertain active vehicle suspension systems, in which sensor failure is taken into account. TakagiSugeno(T-S) fuzzy model is established for considered suspension systems. In order to describe the sensor fault effectively, a corresponding model is introduced. A vital performance index,H_∞ performance, is utilized to measure the drive comfort. In the framework of Kalman-Yakubovich-Popov theory, the H_∞ norm from external perturbation to controlled output is optimized effectively in the frequency domain of 4 Hz-8 Hz to enhance ride comfort level. Meanwhile, three suspension constrained requirements, i.e., ride comfort level, manipulation stability,suspension deflection are also guaranteed. Furthermore, sufficient conditions are developed to design a fuzzy controller to guarantee the desired performance of active suspension systems. Finally, the proposed control scheme is applied to a quarter-vehicle active suspension, and simulation results are given to illustrate the effectiveness of the proposed approach.

Robust Adaptive FTC against Sensor Fault

This paper addresses the problem of robust Fault-Tolerant （FT） design for large-scale systems. This particular class constitutes complex system which can be decomposed into N-interconnected subsystems. Special attention is paid to two different design architectures of an Active Fault-Tolerant Control （AFTC）. An AFTCS is characterized by an online Fault Detection and Isolation （FDI） process and a control reconfiguration mechanism. As the AFTC system offers the possibility to choose different controllers, the controller may be the most appropriate choice for the faulty situation and obtaining better performance. The goal of each adaptive controller is to accommodate sensor anomalies. Continuous, Linear and Time Invariant （LTI） complex system with faulty sensors and external disturbances is proposed. This study focuses on two different internal structures of the system. In this paper the direct adaptive method based on feedback controller design is applied both centralized and decentralized architectures. The controller gain is updated online using an adaptive law which takes into account the estimation of the faults and the disturbances. Then from the both classes of systems structures the adaptation controller performances, in terms of stability and fault effect rejection capability, are studied and compared. The proposed techniques are finally evaluated in the light of a simulation for a centralized interconnected system that can be decomposed into N-subsystems with some strong interconnections.

Robust H-infinity fault-tolerant control for uncertain descriptor systems by dynamical compensators

The problem of robust H-infinity fault-tolerant control against sensor failures for a class of uncertain descriptor systems via dynamical compensators is considered. Based on H-infinity theory in descriptor systems, a sufficient condition for the existence of dynamical compensators with H-infinity fault-tolerant function is derived and expressions for the gain matrices in the compensators are presented. The dynamical compensator guarantees that the resultant colsed-loop system is admissible; furthermore, it maintains certain H-infinity norm performance in the normal condition as well as in the event of sensor failures and parameter uncertainties. A numerical example shows the effect of the proposed method.

ROBUST RELIABLE CONTROLLER DESIGN FOR A CLASS OF TIME-VARYING UNCERTAIN SYSTEMS

The normal H ∞ control design deals with both plant modeling uncertainties and exogenous signal uncertainties by constructing a controller which stabilizes uncertain li near systems while satisfying an H ∞ norm bound constraint on disturbance attenuation for all admissible uncertainties. However, the control design may result in unsatisfactory performances or even instabilities in the event of sensor failures in practical plants. This paper focuses on the problem of the design of robust reliable H ∞ control for a class of time varying uncertainty system with sensor failures. The paper presents a novel technique which deal with this problem by solving three linear matrix inequalities (LMIs). The strict proof guarantees the feasibility of this approach.

STGNN-LMR:A Spatial–Temporal Graph Neural Network Approach Based on sEMG Lower Limb Motion Recognition

Lower limb motion recognition techniques commonly employ Surface Electromyographic Signal(sEMG)as input and apply a machine learning classifier or Back Propagation Neural Network(BPNN)for classification.However,this artificial feature engineering technique is not generalizable to similar tasks and is heavily reliant on the researcher’s subject expertise.In contrast,neural networks such as Convolutional Neural Network(CNN)and Long Short-term Memory Neural Network(LSTM)can automatically extract features,providing a more generalized and adaptable approach to lower limb motion recognition.Although this approach overcomes the limitations of human feature engineering,it may ignore the potential correlation among the sEMG channels.This paper proposes a spatial–temporal graph neural network model,STGNN-LMR,designed to address the problem of recognizing lower limb motion from multi-channel sEMG.STGNN-LMR transforms multi-channel sEMG into a graph structure and uses graph learning to model spatial–temporal features.An 8-channel sEMG dataset is constructed for the experimental stage,and the results show that the STGNN-LMR model achieves a recognition accuracy of 99.71%.Moreover,this paper simulates two unexpected scenarios,including sEMG sensors affected by sweat noise and sudden failure,and evaluates the testing results using hypothesis testing.According to the experimental results,the STGNN-LMR model exhibits a significant advantage over the control models in noise scenarios and failure scenarios.These experimental results confirm the effectiveness of the STGNN-LMR model for addressing the challenges associated with sEMG-based lower limb motion recognition in practical scenarios.

Reliable H-infinity filtering for linear systems with sensor saturation

In this paper, a new reliable H-infinity filter design problem is proposed for a class of continuous-time sys- tems with sensor saturation and failures. Attention is focused on the analysis and synthesis problems of a full order reliable H-infinity filter such that the filtering error dynamics is asymptotically stable with a guaranteed disturbance rejection atten- uation level -y. It is shown that the filtering error dynamics obtained from the original system plus the filter can be modeled by a linear system with sector bounded nonlinearity. The design conditions are given in terms of solutions to a set of linear matrix inequalities （LMIs）. These conditions are then considered in a convex optimization problem with LMIs constraints in order to design an optimal reliable H-infinity filter. A numerical example is given to illustrate the effectiveness of the proposed results.

Active fault-tolerant control of rotation angle sensor in steer-by-wire system based on multi-objective constraint fault estimator

Purpose–Steer-by-wire(SBW)system mainly relies on sensors,controllers and motors to replace the traditionally mechanical transmission mechanism to realize steering functions.However,the sensors in the SBW system are particularly vulnerable to external influences,which can cause systemic faults,leading to poor steering performance and even system instability.Therefore,this paper aims to adopt a fault-tolerant control method to solve the safety problem of the SBW system caused by sensors failure.Design/methodology/approach–This paper proposes an active fault-tolerant control framework to deal with sensors failure in the SBW system by hierarchically introducing fault observer,fault estimator,fault reconstructor.Firstly,the fault observer is used to obtain the observation output of the SBW system and then obtain the residual between the observation output and the SBW system output.And then judge whether the SBW system fails according to the residual.Secondly,dependent on the residual obtained by the fault observer,a fault estimator is designed using bounded real lemma and regional pole configuration to estimate the amplitude and time-varying characteristics of the faulty sensor.Eventually,a fault reconstructor is designed based on the estimation value of sensors fault obtained by the fault estimator and SBW system output to tolerate the faulty sensor.Findings–The numerical analysis shows that the fault observer can be rapidly activated to detect the fault while the sensors fault occurs.Moreover,the estimation accuracy of the fault estimator can reach to 98%,and the fault reconstructor can make the faulty SBW system to retain the steering characteristics,comparing to those of the fault-free SBW system.In addition,it was verified for the feasibility and effectiveness of the proposed control framework.Research limitations/implications–As the SBW fault diagnosis and fault-tolerant control in this paper only carry out numerical simulation research on sensors faults in matrix and laboratory/Simulink,the subsequent hardware in the loop test is needed for further verification.Originality/value–Aiming at the SBW system with parameter perturbation and sensors failure,this paper proposes an active fault-tolerant control framework,which integrates fault observer,fault estimator and fault reconstructor so that the steering performance of SBW system with sensors faults is basically consistent with that of the fault-free SBW system.

An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme

Purpose–Automated driving systems(ADSs)are being developed to avoid human error and improve driving safety.However,limited focus has been given to the fallback behavior of automated vehicles,which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure.Therefore,this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.Design/methodology/approach–Owing to an undetected area resulting from a front sensor malfunction,the proposed ADSfirst creates virtual vehicles to replace existing vehicles in the undetected area.Afterward,the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle;an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure,avoiding potential collisions with surrounding vehicles.This fallback approach was tested in typical cases related to car-following and lane changes.Findings–It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure,revealing that the proposed method is effective for the test scenarios.Originality/value–This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure,and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure.This proposal gives a different view on the fallback safety problem from the normal strategy,in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.