Guest Speaker: Prof. Ramin Sedaghati
Department of Mechanical, Industrial and Aerospace Engineering,
Concordia University
Prof. Sedaghati is a Full Professor of Mechanical Engineering in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University and holds a Tier 1 Concordia Research Chair in Smart Materials and Adaptive Structures. His primary research interests include magnetorheological materials, adaptive structures, adaptive vibration absorbers, magnetoactive soft robots, active vibration control, and structural optimization. He is a Fellow of the Engineering Institute of Canada (EIC), a Fellow of the Canadian Society for Mechanical Engineering (CSME), a Fellow of the American Society of Mechanical Engineers (ASME), and an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). Prof. Sedaghati serves as the Specialty Chief Editor of Frontiers in Materials – Smart Materials and as an Associate Editor of the Journal of Intelligent Material Systems and Structures. He also served as the General Chair of the 30th International Conference on Adaptive Structures and Technologies (ICAST2019) and the 19th International Conference on Electrorheological Fluids and Magnetorheological Suspension (ERMR2025).
Abstract
Magnetoactive elastomers (MAEs) also known as magnetorheological elastomers (MREs) are smart multifunctional materials composed of soft ferromagnetic micron-sized particles (CIPs) dispersed within a low-modulus polymeric matrix. While the field-dependent viscoelastic properties of MAEs have been widely studied, current knowledge of their field-dependent magnetostriction and actuation properties remains limited. Hard magnetoactive elastomers (H-MAEs) are an emerging class of MAEs in which soft magnetic particles are replaced by hard magnetic particles, such as neodymium–iron–boron (NdFeB). They combine mechanical flexibility with strong magnetic properties. Magnetoactive soft continuum robots (MSCRs) incorporating H-MAEs can undergo large deformations through interactions between embedded particles and an external magnetic field. Once programmed, their magnetization enables precise shape control under directed magnetic fields, allowing them to perform specific tasks. The objective of this talk is to address the modeling and control of MSCRs in magnetic field gradients. A nonlinear quasi-static model is developed to accurately predict their deformation, accounting for the effects of magnetic torque, magnetic body forces induced by field gradients, and gravitational loading. The model is further extended to capture the behavior of MSCRs in a fluidic environment. A deep neural network is trained on a finite element simulation dataset to rapidly estimate the nonuniform magnetic field magnitude generated by a permanent magnet. The resulting model is used to fine-tune the coefficients of a feedforward–PID control strategy. The MSCR is fabricated and characterized through comprehensive mechanical and magnetic testing, and a dedicated experimental platform is established to evaluate its performance in both ambient and fluidic environments under varying magnetic field gradients and flow conditions. Hardware-in-the-loop (HIL) experiments are conducted using a six-DOF robotic arm to assess the controller’s performance across different MSCR materials and configurations, including cantilevered and guidewire-integrated designs.
