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A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation |
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| Authors: | Feng Ding Huibo Chen Ling Xu Jiyang Dai Qishen Li Tasawar Hayat |
| Institution: | 1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China;2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;3. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, PR China;4. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| Abstract: | Mathematical models are basic for designing controller and system identification is the theory and methods for establishing the mathematical models of practical systems. This paper considers the parameter identification for Hammerstein controlled autoregressive systems. Using the key term separation technique to express the system output as a linear combination of the system parameters, the system is decomposed into several subsystems with fewer variables, and then a hierarchical least squares (HLS) algorithm is developed for estimating all parameters involving in the subsystems. The HLS algorithm requires less computation than the recursive least squares algorithm. The computational efficiency comparison and simulation results both confirm the effectiveness of the proposed algorithms. |
| Keywords: | Corresponding author at: School of Internet of Things Engineering Jiangnan University Wuxi 214122 PR China |
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