Graduation Semester and Year




Document Type


Degree Name

Master of Science in Electrical Engineering


Electrical Engineering

First Advisor

Frank Lewis


In this work Physical Human-Robot Interaction (pHRI) related problems are addressed as a part of the research going on in Assistive Robotics in the University of Texas Research Institute (UTARI). A novel model reference Neuroadaptive controller is designed based on existing technology of Lewis et al. Adaptive impedance control is an important method for force/motion control of robotic systems. Most of the work in adaptive impedance control makes the error dynamics of the system to look like a prescribed model. By contrast, this work does not want the error dynamics to be prescribed as an impedance model. It makes the robot itself feel like a prescribed impedance model. Model reference behavior is introduced which allows to control the interaction behavior by changing the parameters suitably in the model. The novel controller makes the nonlinear dynamics of the robot to look like a linear model which will be more convenient for humans to interact with during task manipulation. In this thesis a new inner-outer loop Neuroadaptive controller is formulated that makes pHRI robust to changes in both co-robot and human user. First, an inner-loop controller with guaranteed robustness and stability causes a robot to behave like a prescribed robot impedance model. Second, an outer-loop controller tunes the impedance model so that the robot system assists humans with varying levels of skill to achieve task-specific objectives. The controller developed in this work has been proved to work and to facilitate the illustration of the theories established, extensive simulations have been carried out on a 2 DOF robotic arm. The simulations results show good agreements with the theoretical concepts.


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington