Sensor fault estimation based on the constrained zonotopic Kalman filter

For solving the additive and multiplicative sensor faults in the constrained system with unknown but bounded noise, the constrained zonotopic Kalman filter-based additive sensor fault estimator and multiplicative sensor fault estimator are designed, respectively. In the fault estimation process, the states of the system are contained in the zonotopic sets estimated by the constrained zonotopic Kalman filter. The fault detection is carried out by judging whether the true value of the system's output is within the upper and lower bounds of its estimated zonotopic set. Once a fault is detected in the system, the corresponding sensor fault estimator can be used to estimate the additive or multiplicative sensor faults, and the corresponding zonotopic set and interval set can be obtained, respectively. Finally, comparative analyses of the proposed constrained zonotopic Kalman filter-based fault estimators and the zonotopic Kalman filter-based fault estimators in both numerical and case simulations prove the feasibility and effectiveness of the proposed algorithm and show the advantages of this algorithm in view of conservativeness.

Automated summary

This study presents constrained zonotopic Kalman filter-based fault estimators for additive and multiplicative sensor faults in constrained systems. Fault detection is achieved by checking if the output value falls within the estimated interval. Experimental results show the effectiveness of the algorithm and its advantages.