Abstracting the Traffic of Nonlinear Event-Triggered Control Systems

Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks. In previous work, finite-state abstractions were created, capturing the sampling behavior of linear time-invariant (LTI) ETC systems with quadratic triggering...

Promises of Deep Kernel Learning for Control Synthesis

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

Payoff distribution in robust coalitional games on time-varying networks

In this article, we consider a sequence of transferable utility coalitional games, where the actual coalitional values are unknown but vary within known bounds. As a solution to the resulting family of games, we formalize the notion of 'robust core.' Our main contribution is to design two distributed algorithms, namely 1) distributed payoff...

Stable Adaptation in Multi-Area Load Frequency Control under Dynamically-Changing Topologies

Multi-area load frequency control (LFC) selects and controls a few generators in each area of the power system in an effort to dampen inter-area frequency oscillations. To effectively dampen such oscillations, it is required to enhance and lower the control activity dynamically during operation, so as to adapt to changing circumstances....