Goals and Overview
Our long-term goal is to establish a firm foundation to create innovative algorithms to systematically design resilient and intelligent controllers for a wide range of dynamical systems with nonlinear and hybrid nature. Examples of these systems include, but are not limited to, (1) autonomous robots for disaster response and industrial applications, (2) cooperative multiagent systems with distributed and decentralized control policies, (3) walking and running robots with human/animal morphology, (4) complex systems, and (5) wearable and rehabilitation robots like prostheses and orthoses to improve the quality of life for persons with disabilities.
Our research is interdisciplinary and transformative. It draws upon robotics, control theory, machine learning, cyber-physical systems, and optimization to transform state-of-art methods for the control of hybrid dynamical systems with two specific objectives: (1) Creating algorithms to systematically design robust and intelligent controllers for high-dimensional and complex hybrid dynamical systems; and (2) Transferring the control framework into practice with experimental and highly dynamic robotic systems in the HDSRL research laboratory. These algorithms will advance knowledge in the design of feedback controllers for hybrid systems arising from robotic systems. The theoretical innovations also offer a unique opportunity to advance robot locomotion, robot-assisted walking, human-robot interaction, bio-inspired robotic technologies, and high tech tools for disaster response.
The past few years have seen an accelerated effort to design rehabilitation and emergency response robots and to develop robots with human and animal traits. Legged locomotion is extremely important in this advancement. Legged robots can climb stairs, step over gaps in terrain, and are more effective in uneven environments than wheels. The study of legged locomotion has been motivated by the desire to allow people with disabilities to walk and to assist humans in hazardous environments. Legged robots that can perform at this level do not yet exist, and part of what is holding back their development and deployment is adequate feedback control theory. While the technology involved in robot construction is advancing rapidly, there is a fundamental gap in knowledge in feedback control theory for stabilizing complex dynamical models of these increasingly sophisticated legged machines.