The Bio-inspired X-Structure/Mechanism Approach for Exploring Nonlinear Benefits in Engineering

This book presents a unique approach to the design and analysis of beneficial nonlinearity, which can take an important and critical role in engineering systems and thus cannot be simply ignored in structural design, dynamic response analysis, and parameter selection. A key issue in the area is thus systematically addressed about how to analyze and design potential nonlinearities introduced to or inherent in a system of under study, which is a must-do task in many practical applications involving vibration control, energy harvesting, sensor systems and robots etc. This book, therefore, presents an up-to-date summary on the most recent development of a cutting-edge method for nonlinearity manipulation and employment developed in recent several years, named as the X-shaped structure or mechanism approach. The method is inspired from animal leg/limb skeletons and can provide passive low-cost high-efficiency adjustable and beneficial nonlinear stiffness (high static & ultra-low dynamic), nonlinear damping (dependent on resonant frequency and vibration excitation amplitude) and nonlinear inertia (low static & high dynamic) individually or simultaneously. The X-shaped structure or mechanism is a generic structure or mechanism representing a class of beneficial geometric nonlinearity with realizable and flexible linkage mechanism or structural design of different variants or forms (quadrilateral, diamond, polygon, K/Z/S/V-shape, or others) which all share similar geometric nonlinearity and thus similar nonlinear stiffness/damping properties, flexible in design, and easy to implement. This book systematically reviews the research background, motivation, essential bio-inspired ideas, advantages of this novel method, beneficial nonlinear properties in stiffness, damping and inertia, and potential applications, which have been developed ever since 2010. This book is to present unique insight into energy-saving robust control by employing nonlinear X-dynamics in active vibration control systems, and also to demonstrate some other potential applications of the passive (or active) X-structure/mechanism in robot design and control or metamaterials etc.

Prof. Xingjian Jing received his B.S. degree from Zhejiang University, China, M.S. degree and Ph.D. degree in Robotics from Shenyang Institute of Automation, Chinese Academy of Sciences, China, respectively. He also achieved a Ph.D. degree in nonlinear systems and signal processing from University of Sheffield, U.K.. He is now a Professor with the Department of Mechanical Engineering, City University of Hong Kong, and his current research interests are generally related to Nonlinear Dynamics, Vibration, Control and Robots, focusing on theory and methods for employing nonlinear benefits in engineering, including nonlinear frequency domain methods, nonlinear system identification or signal processing, vibration control, robust control, sensor technology, energy harvesting, nonlinear fault diagnosis or information processing, bio-inspired systems and methods, bio-inspired robotics and control etc. He is the recipient of a series of academic and professional awards including 2016 IEEE SMC Andrew P. Sage Best Transactions Paper Award, 2017 TechConnect World Innovation Award in US, 2017 EASD Senior Research Prize in Europe and 2017 the First Prize of HK Construction Industry Council Innovation Award. He has published more than 200 refereed papers and obtained about 30+ patents filed in China and US. He currently serves as Associate Editors for IEEE Transactions on Systems Man Cybernetics-Systems, Associate Editor of IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing, and Nonlinear Dynamics. He is the Specialty Chief Editor on Vibration Systems of Frontiers in Mechanical Engineering, and was Technical Editor of IEEE/ASME Transactions on Mechatronics, and Lead Editor of special issues on ‘Exploring nonlinear benefits in engineering’ published in Mechanical Systems and Signal Processing in 2018/2019 and 2021/2022.