Robust adaptive Kalman filter for structural performance assessment
Shenglun Yi,
Tingli Su,
ZhenYun Tang
Abstract:SummaryStructural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive curren… Show more
Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously.
Under the theoretical support of fuzzy sets theory and confidence index, the proposed approach involves the development of a high‐order robust control method that incorporates optimization techniques to address uncertain systems. First, the utilization of fuzzy sets theory is employed to establish a fuzzy dynamical model. Second, the design of a high‐order robust controller is performed. Third, the design parameters are optimized based on confidence index, and the values are found that minimize the control cost. The simulation results show that this high‐order robust control has significant advantages in dealing with uncertainty, as well as minimizing the control cost. It can be ensured the uniform boundedness and uniform ultimate boundedness. Thus, this proposed method offers the advantage of achieving both robustness and optimality simultaneously.
In this paper, we propose a novel switched approach to perform smartphone-based pedestrian navigation tasks even in scenarios where GNSS signals are unavailable. Specifically, when GNSS signals are available, the proposed approach estimates both the position and the average bias affecting the measurements from the accelerometers. This average bias is then utilized to denoise the accelerometer data when GNSS signals are unavailable. We test the effectiveness of denoising the acceleration measurements through the estimated average bias by a synthetic example. The effectiveness of the proposed approach is then validated through a real experiment which is conducted along a pre-planned 150 m path.
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