Hysteresis Characterization and Compensation of Smart Material-Based Actuators via a New Modified Bouc-Wen Model

Doctor of Philosophy Hysteresis Characterization and Compensation of Smart Material-Based Actuators via a New Modified Bouc-Wen Model by Mohd Hanif Bin Mohd RAMLI Most smart material based actuators (smart actuators) are known for their prominent characteristics of a high resolution of positioning, high bandwidth, and the ease of integration in miniaturized systems. However, their applications are restricted by the inherent hysteresis nonlinearity. This thesis presents an alternative modification to the original Bouc-Wen (BW) model in order to improve the characterization of smart actuators those are affected by hysteresis effects. The modified BW model is formulated in the discrete-time domain. The extended particle swarm optimization technique (EPSO) is used to properly validate the proposed model. Through the simulation study, it is observed that proposed model is capable of describing rate-dependent input-output relations which is an important feature in the modeling of hysteresis phenomenon. Then, the proposed model is directly used in developing control strategies to mitigate the hysteresis effects. In this case, two control architectures are developed; a discrete nonlinear prescribed performance control (DPPC) scheme and a discrete model reference adaptive control strategy (DMRAC). In addition, theoretical analysis of the closed-loop system’s stability under each control algorithm is also systematically discussed. Finally, the efficacy of formulated control strategies are verified via real case applications. The simulation and experimental results substantiate the capacity of the proposed MBW model. It is not only applicable for modeling and characterization, but also towards control development for the betterment of motion tracking problems in smart actuators that are affected by hysteresis effects. ii Acknowledgements "In the name of God, most Gracious, most Compassionate". During the past three years, I had learned technical skills and worked around nice colleagues. The most important is that this experience offers me the opportunity to grow as a person, both professionally and personally. There are many people I want to thank for their support during the time I spent on my research, without their help and support I would never have made it. First of all, I would like to thank my supervisor Professor Xinkai Chen. I feel grateful that he gives me the opportunity to pursue a Ph.D. degree. I want to thank him for the guidance, support, patience and criticism that he gave me during my research. I also thank him for creating such a nice research environment, where he was always there for discussions whenever I needed. It would not have been possible to conduct the research that presented here without his help, suggestions and ideas. My special gratitude goes out to my wife, Norlizan, for her love, source of my inspiration to complete this work. Furthermore, I would like to thank my parents for all the sacrifices they have had to make as I achieve my dreams. A very special thank you to my colleagues, Linh Manh Nguyen, and Chen’s Laboratory members for all the support they have given me over the years. Last but not least, I would like to convey my gratitude to Ministry of Education (KPM) and Universiti Teknologi MARA (UiTM) for their financial supports. Thank you very much.