Distributed Multi-Agent Pathfinding

Multi-agent path finding (MAPF) is the task of finding non-conflicting paths for multiple agents that operate in a environment with shared resources. <br/>Finding an optimal solution quickly becomes intractable for many applications and consequently suboptimal methods are also explored extensively in literature. <br/>This work presents the...

Reinforcement Learning Based Real-time Railway Timetable Scheduling

The railway timetable rescheduling problem is a challenging problem in both industry and academia. It is required to calculate a feasible and relatively good timetable within a limited time to reduce the negative impact of disturbances or disruptions. The railway timetable rescheduling problem is typically formulated as a mixed integer linear...

Freeway Traffic Control - An adaptive control approach towards easy-to-implement Variable Speed Limit algorithms

Nowadays, the high demand for road transportation often reaches a point where it exceeds the capacity of freeway traffic networks, resulting in congestion. Freeway traffic congestion is a major social problem, as it is the reason for increased time delays, higher accident risk and environmental pollution. There is, therefore, the need for a...

Studies on coordinated traffic control: Using variable speeds and testing demand predictions

Urban mobility is challenged with increasing demand, while at the same time reducing emissions, without leading to an unpleasant living environment. Reducing the number of stops on the urban arterial controlled by the coordinated traffic controller can provide (part of) the solution to these challenges for urban mobility. Coordinated traffic...

Learning Drivers’ Preferences in Delivery Route Planning: an Inverse Optimization Approach

Optimizing delivery routes is a well-researched topic, however, most of the classical approaches do not incorporate preferences of drivers, as those approaches focus on minimizing the time or distance of the routes. As a result, the actual driven route of an experienced driver often deviates from the proposed route since the drivers have tacit...

Maneuvering and Platooning of Automated Vehicles via Comprehensive Predictive Control: A Numerical Analysis

Ongoing research in autonomous driving currently focuses on creating new applications for autonomous vehicles (AV) and connected autonomous vehicles (CAV). Specifically, motion planning and control solutions are being developed based on the combination of Artificial Potential Functions (APF) with economic Model Predictive Control (eMPC). These...

Distributed Vibration Control for Robotic cantilever beams: Study of optimal control architectures for robotic metamaterials with relative measurements

Vibrations and disturbances are becoming more of a concern as lightweight, flexible structures in high-tech systems are pushed towards faster speeds and higher precision. Active Vibration Control (AVC) methods have been effectively used to attenuate vibrations and increase the bandwidth of these systems. With the miniaturisation of electronics,...

Traffic network management: "Comparing algorithms for network-wide traffic management using Eclipse SUMO: A pragmatic approach versus Model Predictive Control"

The need for smart traffic control has grown over the last years. Initiated by an increased amount of traffic. Network-wide traffic control is becoming a more interesting field for traffic control. Mainly because computer power has increased and optimisation techniques improved. Network-wide traffic aims to improve the overall traffic state by...

A Self-Configurable and Self-Adjustable Digital Twin For a Production Process: A case study at FOCUS-ON

Digital Twins are a key component of Industry 4.0. They are a digital representation of a real-world entity containing both the structure and the dynamics of its real-world counterpart. The use of Digital Twins appears to offer a powerful an compelling application for production processes. A Self-configurable and self-adjustable Digital Twin is...

Conjugate Dynamic Programming

In decision making problems, the ability to compute the optimal solution can pose a serious challenge. Dynamic Programming (DP) aims to provide a framework to deal with a category of such problems, namely ones that involve sequential decision making. By dividing the original control problem into sub-problems and solving it backwards in time,...

Automated modelling of traffic signal controller behaviour from floating car data using Hidden semi-Markov Models: Paving the way for large scale implementation of Green Light Optimal Speed Advisory systems

Research has shown that GLOSA systems can help reduce the emission of $CO_2$ by motor vehicles and improve flow on urban road networks. However, existing GLOSA systems only work in combination with a limited selection of Traffic Signal Controllers (TSCs) and therefore have not been widely implemented. This research describes and evaluates the...

Modeling and Control of a TCPM

Twisted and coiled polymer muscles (TCPMs) show promise to function as artificial muscles, because of their lightweight, low cost, large contraction, and respectively low hysteresis. A TCPM contracts when it is heated and extends when it is cooled. Different modeling and controlling techniques have been implemented. \cite{VanDerWeijde_2019}...

Distributed Model Predictive Control for Multi-Vehicle Autonomous Driving: Cooperative vs. Non-cooperative Control

In this thesis, we consider the problem of controlling multiple autonomous vehicles in a highway scenario, via MPC. By iteratively solving a motion planning OCP, MPC is perfectly suited for unknown dynamic environments, while optimally computing path and vehicle inputs. Moreover, MPC can ensure the satisfaction of collision avoidance constraints...