CURRENT PROJECT
Human Sensorimotor Control
Many tasks that humans perform in our daily lives involve different sources of uncertainties. However, it is interesting and surprising to notice how the central nervous system (CNS) can coordinate gracefully our movements to deal with these uncertainties. Research in this area allows to gain further insight into this problem and may even provide new methodologies for the diagnosis and treatment of neurodegenerative genetic disorder that affects muscle coordination, such as Parkinson’s disease and Huntington’s disease.
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Robust Adaptive Dynamic Programming
Nonlinear Control of Dynamic Networks
Data-Driven Adaptive Optimal Control of Connected Vehicles
A data-driven non-model-based approach is proposed for the adaptive optimal control of a class of connected vehicles, composed of n human-driven vehicles only transmitting motional data and an autonomous vehicle in the tail receiving the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. By employing adaptive dynamic programming (ADP) technique, optimal controllers are obtained without relying on the knowledge of system dynamics. The effectiveness of the proposed approaches is demonstrated via online learning control of connected vehicles in the Paramics’ traffic micro-simulation.
Traffic Simulation Architecture
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Vehicular Platoon
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