— Multi-agent coordination problems can be cast as distributed optimization tasks. Probability Collectives (PCs) are techniques that deal with such problems in discrete and conti...
— In order to reduce the computational load of the recursive least squares (RLS) algorithm, a decomposition based least squares algorithm is developed for non-uniformly sampled m...
— Models of biochemical reaction networks can be decomposed into a stoichiometric part and a kinetic part. The stoichiometric part describes the structural mass flows while the ...
— We propose a new family of classification algorithms in the spirit of support vector machines, that builds in non-conservative protection to noise and controls overfitting. O...
Abstract— In this paper we consider several problems involving control with limited actuation and sampling rates. Event-based control has emerged as an attractive approach for ad...
Abstract— This paper proposes a novel robust output feedback controller for an electromechanical system in the presence of external disturbance and uncertainties of physical para...
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
— Timed Continuous Petri Net (TCPN) systems are piecewise linear models with input constraints that can approximate the dynamical behavior of a class of timed discrete event syst...
— In this paper we give conditions that a discrete time switched linear systems must satisfy if it is stable. We do this by calculating the mean and covariance of the set of matr...