Zhihong received his B.E. degree from Shanghai Jiaotong University, China, in 1982, M.Sc degree from Chinese Academy of Sciences in 1987, and PhD degree from the University of Melbourne, Australia, in 1994. From 1994 to 1996, Zhihong was the Lecturer in the Department of Computer and Communication Engineering at Edith Cowan University, Perth, Australia. From 1996 to 2001, he was the Lecturer and then Senior Lecturer in the School of Engineering at The University of Tasmania, Australia. In 2001, he was the Visiting Senior Fellow in the School of Computer Engineering at Nanyang Technological University (NTU), Singapore. From 2002 to 2007, he was the Associate Professor of Computer Engineering at NTU. From 2007 to 2008, Zhihong was with Monash University Sunway Campus served as the Professor and Head of Electrical and Computer Systems Engineering, the Chair of the Research Committee of the School of Engineering, and the Chair of the Monash Sunway Campus Research Committee. Zhihong is currently the Professor of Robotics and Mechatronics in the School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. Zhihong’s research interests are in nonlinear control, signal processing, robotics, neural networks, fuzzy systems, engineering optimization, diagnosis of industrial systems, vehicle dynamics and control. He has published more than 250 papers in refereed international journals and refereed international conferences proceedings. His research results have been cited more than 12000 times. Zhihong was the Guest Editor of 8 Special Issues on neural networks, diagnosis of industrial systems and nonlinear control, published in ISCI journals. Zhihong has been involved in many international conferences in control, robotics, signal processing, neural networks and industrial electronics as General Chair, Program Committee Chair, Track Chair, Session Chair, and the International Advisory Committee and Technical Committees member.
A new theoretical framework of parameter estimation of linear systems
In this talk, a brand-new methodology to estimate unknown system parameters for a broad class of second-order linear systems by using the steady state sinusoidal output measurements is proposed. It is shown that, for parameter estimation, the input to a linear system can be chosen as the linear combination of a group of sinusoids. The amplitudes of the steady state sinusoidal output components can be extracted from the system output measurements. The unknown system parameters can then be estimated in the frequency domain with the amplitudes information of steady state sinusoidal output components. The advantages of this method are two folds: (i) Only the steady state output measurements in a fundamental period are required for determining the amplitudes of the steady state output sinusoidal components; (ii) The system parameters can then be estimated within the finite frequency band of the steady state output sinusoidal components. Most importantly, this new method provides a global optimization mechanism, within a fundamental period, to accurately estimate unknown system parameters in the frequency domain. A simulation example for second-order systems with the minimum phase and non-minimum phase, respectively, are implemented to show the effectiveness of the new parameter estimation methodology.