Graduation Semester and Year
2018
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Frank Lewis
Second Advisor
Daniel S Levine
Abstract
Motivated by recent advancement in neurocognitive in brain modeling research, multiple model-based Q-learning structures are proposed for optimal tracking problem of time-varying discrete-time systems. This is achieved by utilizing a multiple-model scheme combined with adaptive resonance theory (ART), and dopamine-like model. In the ART algorithm , dopamine-like model and generates sub-models based on the match-based clustering method utilizing. A responsibility signal governs the likelihood of contribution of each sub-model to the Q-function. The Q-function is learned using the batch least-square algorithm. Simulation results are added to show the performance and the effectiveness of the overall proposed control method. A novel enhanced human-robot interaction system based on model reference adaptive control is presented. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.
Keywords
Cognitive control, Control systems, Human-robot interaction, Bio-inspired control
Disciplines
Electrical and Computer Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Alqaudi, Bakur, "BIO-INSPIRED ADAPTIVE TUNING OF HUMAN-ROBOT INTERFACES" (2018). Electrical Engineering Dissertations. 342.
https://mavmatrix.uta.edu/electricaleng_dissertations/342
Comments
Degree granted by The University of Texas at Arlington