An iterative state-space identification method with data correlation for MIMO systems with measurement noise |
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| Institution: | 1. Department of Computational Mechanics, School of Mechanical Engineering, University of Campinas - UNICAMP, SP, Brazil;2. Department of Industrial Automation, Serra College, Federal Institute of Espirito Santo, ES, Brazil;1. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;2. Electronics Engineering Department, Universidad de Sevilla, Sevilla 41092, Spain;1. School of Mathematical Sciences, University of Jinan, Jinan 250022, China;2. School of Mathematical Sciences, Qufu Normal University, Qufu 273165, China;1. The Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin 150001, China;2. Peng Cheng Laboratory, Shenzhen 518055, China;3. Ningbo Institute of Intelligent Equipment Technology Co., Ltd., Ningbo 315201, China;4. The Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China;1. School of Transportation Science and Engineering, Beihang University, Beijing 100083, PR China;2. Department of Mathematics, Beijing Jiaotong University, Beijing 100044, PR China;3. Texas A & M University at Qatar, Doha 23874, Qatar |
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| Abstract: | This work presents an iterative concept of the State-space Realization Algorithm with Data Correlation (SSRA-DC) to identify MIMO systems with measurement noise and subjected to a reduced number of samples acquired from the process. The measurement noise is characterized as a random signal with properties of white noise and having up to 1% of the output signal amplitude. The proposed technique is based on the Markov parameters matrix’s feedback in an iterative algorithm supported by the SSRA-DC method. A gain factor takes part in the closed-loop to update the Markov parameters matrix, reducing their residues at each iteration. A fixed value for the gain is applied all over the iterations. The Gaussian White Noise (GWN) is employed as the input excitation signal in simulated experiments of mass-damper-springer models with 50 and 100 degrees of freedom. For some algorithm settings, one hundred simulations, each holding more than 100 iterations, are performed to statistically demonstrate the iterative algorithm’s effectiveness compared to the conventional SSRA-DC. Further comparative analysis is accomplished between the iterative method with the ARMAX and N4SID algorithms. |
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