Raybot: Design and control of an underwater quay wall inspection vehicle

Quay walls are important structures that keep the water in harbours and canals within their bounds, and accommodate large infrastructure like roads or ship-handling structures on top of them. Due to their importance it is critical that they do not fail or collapse. Inspections are done to prevent this, as a better knowledge of the state of quay...

Effect of Neural Network Driver Predictions on the Expected Quality of a Model Predictive Control Algorithm for Driving Simulators

RTC strategy reducing the ecological impact of an urban drainage system

Urban wastewater systems can impact the urban ecology by untreated wastewater discharges through combined sewer overflow (CSO) events, or by partial treatment of the wastewater at the wastewater treatment plant (WWTP). CSO events can cause oxygen depletion, eutrophication, and the discharge of pathogens. The partial treatment of the wastewater...

Enabling Hybrid Distributed Model Predictive Control of Traffic Signals In Urban Arterial Networks: An on-street application in industry

Road users still encounter unnecessary delays due to inefficient traffic control in urban traffic networks. These delays are ever-increasing and have large environmental and economic consequences. In the Netherlands, most intersections are controlled using actuated controllers, which respond directly to the current traffic demand obtained from...

Model predictive control for optimum integration of active and passive energy sources

The primary objective of this research is to develop an energy management system for the Co-creation center (CCC) that maximizes the use of passive energy sources while maintaining indoor thermal comfort. Passive energy sources have the potential to significantly reduce the energy consumption of the building. However, to achieve optimal energy...

Integrating human drivers' behavioural rules into microscopic traffic simulation

Autonomous vehicles (AVs) can solve a lot of problems related to traffic safety, comfort and congestion. While the sensors used by these vehicles are getting cheaper, more accurate, and software is improving, the first completely automated vehicle does not exist yet. When AVs start gradually driving on the roads, there will be a mixed traffic...

Model-based Path Planning and Control for Autonomous Vehicles using Artificial Potential Fields

This report presents the results of the graduation thesis from the TU Delft, performed at the Integrated Vehicle Safety department of TNO. The goal of this graduation project is to develop a unified path planning and tracking method for autonomous vehicles in highway driving scenarios, by making use of Artificial Potential Fields (APFs).<br/><br...

Repositioning in shared mobility systems: Combining model predictive control and approximate dynamic programming

The global market for personal mobility has transformed over the last decade. Traditional taxi services have to compete with the emergence of ride-hailing services such as Uber and Lyft. Rapid developments in available algorithms and real-time inter-connectivity of travellers and vehicles offer mobility-on-demand (MOD) services new possibilities...

Enabling GLOSA for on-street operating traffic light controllers

The bottleneck of the maximum road volume in urban areas is the maximum capacity of the traffic flow on the intersection, which is coordinated with Traffic Light Controllers (TLCs). A promising method to decrease the number of stops are Green Light Optimal Speed Advice (GLOSA) systems. These systems will give a speed advice to arriving vehicles...

Realtime Data-driven Learning and Model Predictive Control using Gaussian Processes for Dynamical Systems

Nowadays, machine learning (ML) methods rapidly evolve for their use in model-based control applications. Model-based control requires an accurate model description of the dynamical system to reassure the performance of the controller. Conventionally, this model description is retrieved from first-principles modelling which can be problematic if...

Max-Min-Plus-Scaling Neural Networks to Approximate Continuous Piecewise Affine Model Predictive Control Laws

This thesis extensively examines the influential factors affecting the performance of approximations of Model Predictive Control (MPC) control laws using neural networks. MPC is a control strategy that solves an optimization problem at each timestep. This problem can be computationally complex and could be too slow to compute for online control....

Recursive Subspace Identification with Predictive Control: a Nuclear Norm approach

The main contribution of this thesis is the development of an inherently adaptive controller which recursively implements a system identification and control routine. The main idea here, is that if system identification techniques can be made computationally light, and if they can be combined efficiently with a controller formulation, such a...

Real-Time Predictive Speed Control for Eco-Driving at Signalized Intersections Considering Queue Constraints

Speed trajectories considerably influence vehicular fuel consumption, particularly on signalized roads. To minimize fuel consumption, sharp acceleration/deceleration maneuvers and idling events at signalized intersections should be prevented. By taking advantage of the technological developments in infrastructure-to-vehicle communication, the...

A Multi-Step Gaussian Process Learning Framework for Long-Horizon Vehicle Dynamics Prediction

This work introduces a novel training strategy for Gaussian Process (GP) models aimed at improving their predictive accuracy and uncertainty quantification capabilities over extended prediction horizons. This improvement is highly relevant for applications in model predictive control (MPC) in the autonomous driving domain. Learning-based MPC...