Joint state and fault estimation for time-varying nonlinear systems with randomly occurring faults and sensor saturations

This paper addresses the problem of state estimation and sensor fault reconstruction conjointly for a class of nonlinear systems with time-varying uncertainties for which the nonlinear characteristic satisfies the Lipschitz circumstance. A hybrid approach based on an integral observer and sliding-mode theory has been proposed in order to model sensor fault as a virtual actuator one. For the augmented model, the observer matching condition is not satisfied. To overcome this problem, a new method, which improves the design approach and enhances the rapidity of the fault estimation convergence, has been proposed. The fault estimation error effect is minimized by integrating the [Formula: see text] disturbance attenuation level. The proposed design is formulated and derived as a linear matrix inequality problem. Parameters of this observer are calculated through the linear matrix inequality technique. The proposed method has been validated through an example of a single-link manipulator robot. Simulation results show that this approach can estimate the state and the sensor fault successfully, despite the time-varying uncertainties and the presence of unknown inputs.

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Robust H∞ fault estimation and fault tolerant control for T-S nonlinear systems with time varying delays

2016 8th International Conference on Modelling, Identification and Control (ICMIC) ◽

10.1109/icmic.2016.7804165 ◽

2016 ◽

Cited By ~ 1

Author(s):

Djamel Eddine Cheridi ◽

Noura Mansouri

Keyword(s):

Nonlinear Systems ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Fault Estimation ◽

Time Varying

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Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

Soft Computing ◽

10.1007/s00500-019-04618-8 ◽

2019 ◽

Vol 24 (15) ◽

pp. 11535-11544 ◽

Cited By ~ 1

Author(s):

Haiying Qi ◽

Yiran Shi ◽

Shoutao Li ◽

Yantao Tian ◽

Ding-Li Yu ◽

...

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Fault Tolerant ◽

Sliding Mode ◽

Fault Tolerant Control ◽

System Stability ◽

Fault Estimation ◽

Time Varying ◽

Change Rate ◽

The Impact

AbstractThis paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained.

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