Sensor fault-tolerant control method based on multiple model approach

Author(s):  
D. Theilliol ◽  
J.C. Ponsart ◽  
H. Noura ◽  
D. Sauter
2015 ◽  
Vol 67 (1) ◽  
pp. 133-138
Author(s):  
Ionut Cristian Resceanu ◽  
Cristina Floriana Resceanu

Abstract A fault tolerant control method is proposed for Quanser SRV-02 System in order to maintain the required performance in the presence of sensor failures. The proposed approach integrates control law and a sensor fault tolerance schema. Theoretical analysis and simulation results have confirmed the effectiveness of the proposed method.


2000 ◽  
Vol 33 (11) ◽  
pp. 777-782 ◽  
Author(s):  
Hassan Noura ◽  
Jean-christophe Ponsart ◽  
Didier Theilliol

Author(s):  
Parisa Yazdjerdi ◽  
Nader Meskin

In this article, an actuator fault-tolerant control scheme is proposed for differential-drive mobile robots based on the concept of multiple-model approach. The nonlinear kinematic model of the differential-drive mobile robot is discretized and a bank of extended Kalman filters is designed to detect, isolate, and identify actuator faults. A fault-tolerant controller is then developed based on the detected fault to accommodate its effect on the trajectory-tracking performance of the mobile robot. Extensive experimental results are presented to demonstrate the efficacy of the proposed fault-tolerant control approach.


Author(s):  
Pu Yang ◽  
Zhangxi Liu ◽  
Dejie Li ◽  
Bin Jiang ◽  
Jiaqi Zhu

In this paper, we design a novel sliding mode prediction fault-tolerant control algorithm for multi-delays discrete uncertain systems with sensor fault. The global sliding surface is designed to replace the traditional linear sliding surface as a predictive model to ensure the global robustness of the system. For sensor fault and sliding mode buffeting, a power-dependent function reference trajectory with fault compensation is designed to attenuate chattering and achieve better stability. In the process of rolling optimization, an improved whale optimization algorithm is developed. On the premise of obtaining good convergence speed and accuracy, the optimization process can avoid falling into the local minimum value and solve the problem of premature convergence. Finally, the comparison experiments on the quad-rotor simulation platform prove the rationality and superiority of the algorithm.


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