|Biography: Dr. Davaie Markazi received his BSc and MSc in Mechanical Engineering from Iran University of Technology and Sharif University of Technology in 1982 and 1987, respectively. He received his PhD in Mechanical engineering from McGill University, Canada, in 1995. He is currently a Professor in the School of Mechanical Engineering, Iran University of Science and Technology. Dr. Markazi is one of the founders and the former Chair of the Iranian Society for Mechatronics. His research interests include digital and hybrid control of dynamic systems, adaptive fuzzy sliding control of nonlinear systems, networked control systems, Robotics, and Dynamic Analysis of Brain. Dr. Markazi has published numerous journal and conference papers and two books.
Speech Title: Recent Advances in Adaptive Fuzzy Sliding Mode Control and its Applications
Abstract: Many well-known control approaches have already been developed for control of nonlinear uncertain systems in the literature. More specifically, a broad range of methods, namely, the Adaptive Fuzzy Sliding Mode Control (AFSMC) approach have been proposed, with the core idea of achieving robustness and lesser extent of information about the plant, since 1994. A new class of AFSMC methods have been developed and extended in the Mechatronics Lab of IUST since 2008. The proposed methods have been successfully applied to control of SISO/MIMO chaotic systems, MEMS resonators, Mechanisms with friction, Active vortex-induced vibration control, Pneumatic vibration isolation, Wheel slide protection of locomotives, Input-delayed uncertain systems, Anti-lock break system, Under-actuated AUVs, Proportional navigation of uncertain targets, active vibration control of thick piezo-laminated beams and position/force control of Hexapod robots.
In this talk, a particular case study, namely, the hybrid position/force control of a Hexapod (Stewart Manipulator) using the AFSMC method will be discussed in a bit more detail. The important advantage of the proposed method is the ability to work with a highly simplified dynamic model of the robot, where the ignored part of the model dynamics has been considered as an state-dependent uncertainty. Such a complex problem has been successfully controlled by an extended version of the AFSMC method. Experimental verifications are performed on the position control loop of the Stewart Manipulator to depict the effectiveness.