Recursive parameter identification of the dynamical models for bilinear state space systems |
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| Authors: | Xiao Zhang Feng Ding Fuad E. Alsaadi Tasawar Hayat |
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| Affiliation: | 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education),Jiangnan University,Wuxi,People’s Republic of China;2.College of Automation and Electronic Engineering,Qingdao University of Science and Technology,Qingdao,People’s Republic of China;3.Department of Electrical and Computer Engineering, Faculty of Engineering,King Abdulaziz University,Jeddah,Saudi Arabia;4.Department of Mathematics,Quaid-I-Azam University,Islamabad,Pakistan |
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| Abstract: | This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms. |
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