A continuous finite-time convergence fixed-lag FIR smoother using multiple IIR filters |
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| Affiliation: | 1. Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea;2. Department of Electrical Engineering and Convergence IT Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk 37673, Korea;1. School of Automation, Central South University, Changsha 410083, China;2. Peng Cheng Laboratory, Shenzhen 518000, China;3. Texas A&M University at Qatar, Doha PO Box 23874, Qatar;1. School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100089, China;3. School of Engineering, Deakin University, Geelong, VIC 3217, Australia;4. School of Control Science and Engineering, Dalian University of Technology, Dalian 1160240, China;1. Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, Udine 33100, Italy;2. Systems Research Institute, Polish Academy of Sciences, Ul. Newelska 6, Warsaw 01-447, Poland;1. School of Electrical and Control Engineering, North China University of Technology, Beijing, China;2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China;3. Mechatronics, Embedded Systems and Automation Lab, University of California, Merced, USA |
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| Abstract: | In this paper, we propose a continuous finite-time convergence finite impulse response (FIR) fixed-lag smoother using multiple, or more than two, computationally efficient IIR filters. We describe the optimal design to improve and further optimize an existing scheme based on two IIR filters. Multiple IIR filters are utilized to minimize the estimation error variance of the proposed smoother under the condition that its estimate converges to a real state in a finite time. As the number of adopted IIR filters increases, the proposed smoother improves and its performance approaches that of the heavy computational fixed-lag minimum variance FIR smoother. By choosing the appropriate number of IIR filters, we can balance the trade-off between improved accuracy and increased implementation costs. To realize the optimal design of IIR filters with the limited number of IIR filters, their gains are determined using a particle swarm optimization scheme. Numerical examples are used to show that with an increasing number of IIR filters, the estimation error variance decreases monotonically while guaranteeing finite-time convergence. |
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