This book covers a new paradigm of system modeling – the robust control-oriented linear fractional transformation (LFT) modeling. A dynamic system expressed in LFT modeling framework paves the way for the application of modern robust controller design technique like µ-synthesis method for controller design. This book covers the generalized robust control-oriented LFT modeling representation of the MIMO system depending upon the uncertainty structure, system dynamics, and the dimensions of the input–output. The modeling framework results into a compact and manageable representation of uncertainty modeling in the form of feedback-like structure that is suitable for design and implementation of the robust control technique like µ-synthesis-based H8 control theory. This book also describes the application of the proposed methodology in a variety of advanced mechatronic systems like the Twin Rotor MIMO system, wheeled mobile robot, and an industrial robot arm.
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In this book, the authors investigate robust control-oriented linear fractional transform modelling. More concretely, they provide a systematic approach of mathematical modelling associated with complex physical system that would enable the control system engineer to effectively design H robust controller with guaranteed performance. (Savin Trean, zbMATH 1516.93001, 2023)
Introduction.- Mathematical Modelling of Real Physical System.- Control Oriented Linear Fractional Transformation.- Synthesis Based H8 Control Theory.- Generalized Control Oriented LFT Modelling of a Coupled Uncertain MIMO System.- Control-Oriented LFT Modelling of a Two-DOF Spring- Mass-Dashpot Dynamic System.- Control Oriented LFT Modelling and H8 Control of Twin Rotor MIMO System.- Control Oriented LFT Modelling and H8 Control of Differentially Driven Wheeled Mobile Robot.- Control Oriented LFT Modelling and H8 Control of Differentially Driven Wheeled Mobile Robot with Slip Dynamics.
Dr. Tamal Roy received his Bachelors degree in Electrical Engineering from the West Bengal University of Technology, Kolkata, 2005. He received his Master in Mechatronics Engineering from the National Institute of Technical Teachers Training and Research, Kolkata, in 2008, and completed his Ph.D. from Jadavpur University in 2016. In 2008, he joined the Department of Electrical Engineering at Hooghly Engineering and Technology College as a Lecturer. Since 2011, he has been working as an assistant professor in the Electrical Engineering Department of MCKV Institute of Engineering and presently is working as the head of the Department. His current research interests include adaptive control, uncertainty modeling, system identification, and robust control of nonlinear systems.
Dr. Ranjit Kumar Barai graduated in Bachelor of Electrical Engineering in 1993 and Master of Electrical Engineering in 1995 from Jadavpur University, India, and Ph.D. in Artificial Systems Science (with specialization in Mechatronics and Robotics) in 2007 from Chiba University, Japan. He has performed post-doctoral research at Rolls-Royce Corporate Laboratory at Nanyang Technological University, Singapore, in 2015-16 on robotized manufacturing. He is now a professor in the Control Systems Division, Department of Electrical Engineering, Jadavpur University. He has more than 20 years of working experience in industry, research, and teaching at graduate and post-graduate levels. His research interests include mechatronics, robotics, control systems, machine learning and soft-computing, modeling and system identification, and real-time systems.
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