Vibration, Morphing Aircraft and eVTOLs

Morphing aircraft present a rich area of research from the point of view of general structural dynamics and vibration issues. A brief introduction to morphing is given, followed by identifying some vibration and noise issues. The focus is on eVTOL and UAV aircraft applications. The technologies employed are deep reinforcement learning and mechanical metamaterials. Some past results are reviewed, current results illustrated, and new research areas are suggested. Coupled with learning algorithms, a method for designing smart autonomous morphing airfoils to recover from flow-induce d vibrations is reviewed. A mass-conserved mechanical metamaterial is introduced for both vibration suppression and flow control (delay of separation). Thoughts on future research are also presented.

Short CV

Daniel J. Inman received his Ph.D. from Michigan State University in Mechanical Engineering in 1980 and is the Harm Buning Collegiate Professor and former Chair of the Department of Aerospace Engineering at the University of Michigan. Since 1980, he has published eight books (on vibration, energy harvesting, control, statics, and dynamics), eight software manuals, 20 book chapters, over 425 journal papers and 690 proceedings papers, given 79 keynote or plenary lectures, graduated 71 Ph.D. students, and supervised more than 75 MS degrees. He works in the areas of applying smart materials and structures to solve aerospace engineering problems including energy harvesting, structural health monitoring, vibration suppression and morphing aircraft. He is a Fellow of the American Institute of Aeronautics and Astronautics, American Society of Mechanical Engineers, International Instituted for Acoustics and Vibrations, Society of Experimental Mechanics and American Academy of Mechanics. He holds the 2000 ASME Adaptive Structures Award, the 2003 SPIE Smart Structures and Materials Lifetime Achievement Award, the ASME Den Hartog Award for lifetime achievement in teaching and research in vibration, the 2009 Lifetime Achievement award in Structural Health Monitoring, and the 2014 AIAA Structures, Structural Dynamics, and Materials Award. He served as the Editor-in-Chief of the Journal of Intelligent Material Systems and Structures (1999-2024) and the Journal of Vibration and Acoustics (1989-1999).

 

Multiphysics Modeling, Control, and Learning

Robotic systems are increasingly required to operate with reduced mass, higher efficiency, and greater autonomy while interacting with complex, dynamic environments. These requirements inevitably introduce flexibility, strong multiphysics couplings, and sensing and control challenges that cannot be addressed by rigid-body models alone. This talk presents recent advances in robotic system design and control that integrate multiphysics modeling, advanced control strategies, and data-driven perception.

The first part focuses on lightweight parallel manipulators with flexible links, where inertia reduction improves energy efficiency but induces structural vibrations. A dual-loop control architecture is discussed, combining vision-based feedback for rigid-body motion with vibration attenuation strategies based on both model-based (LQG) and model-free, strain-informed approaches. The results demonstrate how multimodal sensing and hybrid control architectures can effectively mitigate flexibility-induced performance degradation.

The second part addresses bioinspired aquatic robotics, presenting a flexible, fish-like robotic platform actuated by smart materials. A dynamic model derived from Kirchhoff’s equations captures the coupled structural and propulsion dynamics, enabling motion planning and maneuverability analysis through optimal control formulations. This case study illustrates how bioinspired design, flexible dynamics, and optimization-based control can be systematically integrated.
Overall, the talk highlights a unifying perspective on robotics, where multiphysics simulation, control theory, and learning-enabled perception jointly support robust, efficient, and adaptive robotic systems.

Short CV

Maíra Martins da Silva is an Associate Professor in the Department of Mechanical Engineering at the São Carlos School of Engineering, University of São Paulo (EESC-USP), Brazil. She received her B.Sc. (2001) and M.Sc. (2004) degrees in Mechanical Engineering from USP and her Ph.D. (2009) from KU Leuven, Belgium, with financial support from CAPES. She held a postdoctoral position at EESC-USP in 2010 with support from FAPESP and obtained her Habilitation in 2018. She currently coordinates the Graduate Program in Mechanical Engineering at EESC-USP and has coordinated FAPESP-funded research projects in flexible and bioinspired robotics, in addition to participating in a ROTA 2030 project on electric vehicles. Her research interests include multiphysics simulation, robotic control, optimization, and machine learning. She serves as Associate Editor of Expert Systems with Applications and Engineering Applications of Artificial Intelligence, and as Technical Editor of the Journal of the Brazilian Society of Mechanical Sciences and Engineering.