Auxiliary model based least squares parameter estimation algorithm for feedback nonlinear systems using the hierarchical identification principle |
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| Authors: | Peipei Hu Feng Ding Jie Sheng |
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| Affiliation: | 1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, PR China;2. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China;3. Institute of Technology, University of Washington, Tacoma, WA 98402-3100, USA;1. Center for Automation Research, UMIACS, University of Maryland, College Park, MD 20742, United States;2. National Institute of Standards and Technology, Gaithersburg, MD 20899, United States;1. Moulay Ismail University, ENSAM, L2MC, Meknes, Morocco;2. Université de Caen Basse-Normandie, GREYC UMR CNRS, 14032 Caen, France Universitat Politecnica de Catalunya, Barcelona, Spain;1. College of Mechanical and Electronic Engineering, Shanghai Normal University, Shanghai, China;2. Department of Mechanical Engineering, Guangxi University, Nanning, China |
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| Abstract: | This paper presents a decomposition based least squares estimation algorithm for a feedback nonlinear system with an output error model for the open-loop part by using the auxiliary model identification idea and the hierarchical identification principle and by decomposing a system into two subsystems. Compared with the auxiliary model based recursive least squares algorithm, the proposed algorithm has a smaller computational burden. The simulation results indicate that the proposed algorithm can estimate the parameters of feedback nonlinear systems effectively. |
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