Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...

A State-Space Approach to Modelling DC Distribution Systems

Many modelling methods for the analysis of dc distribution grids only consider monopolar configurations and do not allow for mutual couplings to be taken into account. The modelling method presented in this paper aims to deal with both of these issues. A state-space approach was chosen for its flexibility and computational speed. The derived...