ELECTRICAL & COMPUTER ENGINEERING
Zhi-Hong Mao, PhD
1204 Benedum Hall | 3700 O’Hara Street | Pittsburgh, PA 15261
Professor William Kepler Whiteford Faculty Fellow
P: 412-624-9674 maozh@engr.pitt.edu
Human-in-the-Loop Control Systems This direction of research is aimed to understand the capabilities of human operators in human-machine interaction (HMI) and apply this understanding to the design of efficient and robust human-in-the-loop control systems. The complexity of HMI grows rapidly in modern control systems. Highly automated systems often rely heavily on human intervention, supervision, and maintenance. Therefore, it is important and critical to understand the human capabilities and to apply this understanding to control systems design. My current work tries to evaluate quantitatively the capabilities of the human neural system in manual control. We take a comprehensive approach that synergistically combines control theory, information theory, computational neuroscience, non-invasive human experiments, and computer simulations in the study of human-in-the-loop control systems.
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…11101011010 ... Encoder
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Human controller
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Surgery robot
Adaptive Control of Networked and Large-Scale Dynamical Systems This direction of research is to develop an adaptive, learning architecture for control of networked and large-scale dynamical systems such as the power grid. The human brain is the most intelligent controller in nature. It is an ideal source for an enlightening study of advanced control for complex engineering systems. Our research goal is to understand the control and learning principles of the
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human brain and to bring ideas inspired by neural principles to the control of complex engineering systems. Our current work consists of two tasks. The first task is to develop a brain-inspired adaptive control and learning architecture that takes advantages of the brain’s organizational and operational principles. The second task is to apply the brain-inspired control architecture for high-voltage direct-current
(HVDC) power system. This work creates interdisciplinary schemes for biologically inspired engineering design. The brain-like circuits and algorithms can be developed and embedded in the controllers of power systems, robots, and other automatic systems that will have capabilities of self-observation, self-healing, and self-improvement.
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING