DEJAN Milutinović's homepage
My work focuses on the control of robotic and vehicle systems in which the influence of stochastic processes cannot be ignored and needs to be anticipated, for example, the presence of stochastic drifts, measurement and estimation errors and human-in-the loop uncertainty. Stochastic processes can also model any lack of data resulting in a dynamic uncertainty. While one may think of using stochastic models to predict the data and then use the predictions for the control, my approach is to use stochastic processes to model possible dynamic system evolutions and anticipate uncertainties in their control .
This multidisciplinary research is primarily focused on probabilistic models, stochastic dynamics and control with the aim of creating better robotic systems. The main challenges in pursuing this goal are in nonlinearities, multiple degrees of freedom and uncertainties. This work is applicable to robotics, autonomous systems, next generation of air transportation system (NextGen) and biomedical research.
Research interest: Stochastic dynamical systems and statistical signal processing, multi-agent systems/robotics, systems biology/immune system, optimal control, hybrid and discrete event systems
Our paper "Scalable Markov Chain Approximation for a Safe Intercept Navigation in the Presence of Multiple Vehicles" has been accepted for publication in Autonomous Robots
Our paper "Markov Inequality Rule for Switching among Time Optimal Controllers in a Multiple Vehicle Intercept Problem" has been published in Automatica