Closed-loop identification of stable and unstable processes with time-delay |
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| Institution: | 1. Department of Electronics and Communication, PDPM, IIITDM, Jabalpur, Madhya Pradesh, India;2. Department of Electronics and Communication, The LNM Institute of Information Technology, Jaipur, India;1. The Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China;2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China;3. School of Control Engineering, Northeastern University - Qinhuangdao Campus, Qinhuangdao 066004, China;1. State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China;2. Intelligent Research Institute of Chang''an Automobile Co., Ltd, Chongqing, China;1. School of Automation, Southeast University, Nanjing 210096, China;2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096, China;1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;2. Quantum Machines Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan;3. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China |
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| Abstract: | This article concerned with the parametric identification problem of continuous-time process dynamics with unknown time-delay. An improved closed-loop approach is developed to carryout process identification in terms of generalized first and second order plus time-delay (GFOPTD & GSOPTD) process models. These generalized models can include both stable and unstable plant dynamics. The key feature of the proposed method over existing techniques is its effective indirect nature, where the mathematical formulation is developed to identify continuous process dynamics and time-delay parameters, indirectly in terms of discrete parameters. The advantage of the proposed indirect method is that it not only allows to accurately identify process parameters but helps in achieving the single parameter based convergence, which is also proved mathematically. The identification accuracy and robustness of the proposed technique in the presence of measurement noise and modeling uncertainties is corroborated with the help of simulation examples on benchmark problems and by using a practical tank process experiment. |
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