Bakur Alqaudi

ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical Engineering

First Advisor

Frank Lewis

Second Advisor

Daniel S Levine


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.


Cognitive control, Control systems, Human-robot interaction, Bio-inspired control


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington