ABSTRACT
This chapter presents a general online control and optimization framework for hybrid systems. It explores a general-purpose control and optimization framework where controllers are parameterized and the parameters are adaptively tuned online based on observable data. A hybrid system consists of both time-driven and event- driven components. The event-driven paradigm offers an alternative complementary approach to the time-driven paradigm for modeling, sampling, estimation, control, and optimization. Event-driven approaches are attractive in receding horizon control, where it is computationally inefficient to reevaluate a control value over small time increments as opposed to event occurrences defining appropriate planning horizons for the controller. The methodologies developed for sampling, estimation, communication, control, and optimization of dynamic systems have also evolved based on the same time-driven principle. The history of modeling and analysis of dynamic systems is founded on the time-driven paradigm provided by a theoretical framework based on differential equations.
