Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots

Baghernezhad, Farzad; Khorasani, K.

doi:10.1016/j.neucom.2015.06.050

Cited by 33 publications

(18 citation statements)

References 27 publications

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“…In order to better detect faults and isolate fault signals at the site, some methods and strategies that are not model-based are proposed. Methods such as fuzzy logic [13,14], neural network [15,16], and Kalman filter [17] are used to estimate uncertain parameters in nonlinear systems. The performance of model-based fault detection and isolation methods rely on accurate linear system modeling.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

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Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Geng

Чулин

Wang

2020

Sensors

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“…(3) Methods based on mathematical model, such as Chu and Zhang (2014) constructed a sliding mode observer to analyse residual; Stavrou et al (2016) estimated the computational error boundary between the estimated and measured robot states, and when the estimated error exceeded this boundary, faults were detected. Accurate models were usually difficult to obtain, so researchers either adopted robust method to reduce the demand for accurate model (Baghernezhad and Khorasani, 2015), or adopted Bayesian method to detect the existence of faults, like the adaptive Kalman filter method (Wang and Liang, 2019) or particle filter (Hsu et al, 2016; Su et al, 2017) to track the system state. Based on these theories, particle filter method was constructed to track fault state values.…”

Section: Introductionmentioning

confidence: 99%

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

Cao

et al. 2020

Transactions of the Institute of Measurement and Control

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The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

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“…These methods have good performance in detection and identification for nonlinear systems and uncertainty of system models. The representative methods mainly include artificial neural network (ANN) [9,11,12], support vector machine (SVM) [13], and Gaussian process regression (GPR) [14]. The major challenge of this kind of method is how to build an appropriate regressive model depending on the input/output data.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration

Zhang

Zhao

Pang

et al. 2019

Sensors

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For the integration of global navigation satellite system (GNSS) and inertial navigation system (INS), real-time and accurate fault detection is essential to enhance the reliability and precision of the system. Among the existing methods, the residual chi-square detection is still widely used due to its good real-time performance and sensibility of fault detection. However, further investigation on the performance of fault detection for different observational conditions and fault models is still required. In this paper, the principle of chi-square detection based on the predicted residual and least-squares residual is analyzed and the equivalence between them is deduced. Then, choosing the chi-square detection based on the predicted residual as the research object, the influence of satellite configuration and fault duration time on the performance of fault detection is analyzed in theory. The influence of satellite configuration is analyzed from the number and geometry of visible satellites. Several numerical simulations are conducted to verify the theoretical analysis. The results show that, for a single-epoch fault, the location of faulty measurement and the geometry have little effect on the performance of fault detection, while the number of visible satellites has greater influence on the fault detection performance than the geometry. For a continuous fault, the fault detection performance will decrease with the increase of fault duration time when the value of the fault is near the minimal detectable bias (MDB), and faults occurring on different satellite’s measurement will result in different detection results.

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Computationally intelligent strategies for robust fault detection, isolation, and identification of mobile robots

Cited by 33 publications

References 27 publications

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration

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