Modelling and Parameter Estimation of Dynamic Systems

J.R. Raol has B.E. and M.E. degrees in Electrical Engineering from M.S. University of Baroda, and a Ph.D. from McMaster University, Canada. He is a senior member of the IEEE (USA), life Fellow of the Aeronautical Society of India, life member of the System Society of India and senior scientist with National Aerospace Laboratories (NAL). Dr Raol has written around 100 publications and his research interests include multi-sensor data fusion, parameter estimation and artificial intelligence.G. Girija has M.Sc. and Ph.D. degrees from Bangalore University and has written around 50 publications. Dr Girija currently works as a senior scientist at NAL, is a recipient of the NAL Foundation Day Award and is a life member of the Aeronautical Society of India. Her research areas are parameter estimation of aerospace vehicles and multi-sensor data fusion.J. Singh has a Ph.D. in Aerospace Engineering from IIT Kanpur. He is a recipient of the Alexander von Humboldt Fellowship and has worked as a guest scientist at DLR-Institute for Flight Systems, Germany. He is a senior member of the AIAA and a life member of the Aeronautical Society of India and System Society of India. He currently works as a scientist at NAL and is a recipient of the NIL Foundation Day Award. His research areas are aircraft modelling and parameter estimation.

'while this book would be of interest to the general reader, it is of particular appeal to science and engineering students at all levels. It also presents a definite benefit to practising engineers and lecturers who are engaged in parameter estimation work. I found this an enthralling read, which now has a prominent place on my bookshelf - it certainly will be referred to in the future.'

• Chapter 1: IntroductionChapter 2: Least squares methodsChapter 3: Output error methodChapter 4: Filtering methodsChapter 5: Filter error methodChapter 6: Determination of model order and structureChapter 7: Estimation before modelling approachChapter 8: Approach based on the concept of model errorChapter 9: Parameter estimation approaches for unstable/augmentedChapter 10: Parameter estimation using artificial neural networks and geneticChapter 11: Real-time parameter estimationAppendices