Tham khảo tài liệu 'frontiers in adaptive control part 2', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 16 Frontiers in Adaptive Control van der Schaf A. 2000 . L2-Gain and Passivity Techniques in Nonlinear Control Spriger-Verlag ISBN 978-1852330736. Xu Y. Kanade T. 1993 . Space Robotics Dynamics and Control Kluwer Academic Publishers ISbN 978-0792392651. Xu Y Shum Lee . Kanade T. 1992 . Adaptive Control of Space Robot System with an Attitude Controlled Base Proc. of the 1992 Int. Conf on Robotics and Automation pp. 2005 - 2011 Nice France May 1992. 2 On-line Parameters Estimation with Application to Electrical Drives Navid R. Abjadi1 Javad Askari1 Marzieh Kamali1 and Jafar Soltani2 -Isfahan University of Tech. 2Islamic Azad University- Khomeinishar Branch Iran 1. Introduction The main part of this chapter deals with introducing how to obtain models linear in parameters for real systems and then using observations from the system to estimate the parameters or to fit the models to the systems with a practical view. Karl Friedrich Gauss formulated the principle of least squares at the end of the eighteenth century and used it to determine the orbits of planets and asteroids Astrom Wittenmark 1995 . One of the main applications of on-line parameters estimation is self-tuning regulator in adaptive control nevertheless other applications such as load monitoring or failure detection estimation of some states to omit corresponding sensors and etc. also have great importance. 2. Models linear in parameters A system is a collection of objects whose properties we want to study and a model of a system is a tool we use to answer questions about a system without having to do an experiment Ljung Glad 1994 . The models we work in this chapter are mathematical models relationships between quantities. There are different mathematical models categories such as Ljung Glad 1994 Deterministic-Stochastic Stochastic models despite deterministic models contain stochastic variables or processes. Deterministic models are exact relationships between variables without uncertainty. .
