Recursive filtering for state‐saturated systems with randomly occurring nonlinearities and missing measurements |
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| Authors: | Chuanbo Wen Zidong Wang Jun Hu Qinyuan Liu Fuad E Alsaadi |
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| Affiliation: | 1. College of Electrical Engineering, Shanghai Dianji University, Shanghai, China;2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China;3. Department of Computer Science, Brunel University London, Uxbridge, UK;4. Department of Applied Mathematics, Harbin University of Science and Technology, Harbin, China;5. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China;6. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia |
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| Abstract: | This paper deals with the filtering problem for a class of discrete‐time state‐saturated systems subject to randomly occurring nonlinearities and missing measurements. A set of mutually independent Bernoulli random variables is used to describe the random occurrence of the missing measurements. Due to the simultaneous consideration of the state saturation, the randomly occurring nonlinearities, and the missing measurements, it is extremely hard to calculate the actual filtering error covariance in a closed form. As such, the objective of this paper is to construct an upper bound for the filtering error covariance and then design the filter parameters to minimize such an upper bound. The performance of the proposed filters is analyzed in terms of boundedness and monotonicity. Specially, we have shown that the minimum upper bound is always bounded under a mild assumption. Moreover, the relationship between the estimator performance and the arrival probability of the measurements is discussed. A numerical simulation is used to demonstrate the effectiveness of the filtering method. |
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| Keywords: | difference equations filter performance missing measurements recursive filtering state saturation |
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