Application Guidelines

If you are an MSc student and you want to start a MSc project with me please send me an e-mail including:

  1. a short CV (1/2 – 1 page),
  2. BSc & MSc transcripts,
  3.  a brief motivation letter stating:
    1.  What is your MSc program and specialization,
    2. What you are interested in (topic and/or application),
    3. The type of assignment you are interested in (more theoretical/more applied),
    4. The intended starting date,
    5. Your relevant experience (studies, technical projects, internships, hobbies),
    6. Programming languages and related (C, C++, ROS, etc.).

A background in control theory, optimization, and/or cryptography is recommended for most of the projects.

Available MSc Projects

A graduation project with HAN Automotive Research is currently available. Read more information here.

Current MSc Students

MPC for quay wall inspection robot

Tijmen van Enckevort's MSc Thesis

Abstract

Supervisors

  • Laura Ferranti
  • Reka Berci-Hajnovics (DoBots)

Privacy-preserving Distributed Consensus

Thijs Niesten's MSc Thesis

Abstract

Supervisors

  • Laura Ferranti
  • Lorenzo Lyons

Automatic Tuning of an MPCC-based Motion Planner

Weiming Chen's MSc Thesis

Abstract

Supervisors

  • Laura Ferranti
  • Oscar de Groot

Former MSc Students

Optimization based 2D platoon control with multiple behaviors implemented through a finite state machine

Shuhul Razdan's MSc Thesis

Abstract

With inland transportation increasing every passing day, vehicle platooning offers a good solution towards travelling more efficiently. Along with reducing traffic congestion on roads, platooning also leads to better fuel consumption among vehicles, fewer accidents, and most importantly, vehicle platoons can be made autonomous using optimization-based control techniques, such as Model Predictive Control (MPC). Much research has been done towards optimise fuel consumption through slip-streaming and by using topological data. A separate research field also looks into performing lanes changes and avoiding obstacles. However, both these research fields are disjointed and use a different model and MPC formulations to employ control. This project aims at developing an unified system that can implement longitudinal control (fuel optimisation), formation reconfiguration (lane changes) and collision avoidance using one model and MPC formulation. The platoon switches between these behaviours depending on the environment around the platoon. This report also highlights the limitations of the controller and gives concrete recommendations on how to deal with the shortcomings.

Supervisors

  • Laura Ferranti
  • Berend Kupers (TNO)
  • Sander Wahls (DCSC)

A Unified Approach to Motion Planning and Low-Level Control of Autonomous Vehicles in Urban Environments

Claudio Molteni's MSc Thesis

Abstract

Supervisors

  • Laura Ferranti
  • Francesco Braghin (PoliMi)

Optimization-based Fault Mitigation in Automated Driving

Niels Lodder's MSc Thesis

Abstract

With increased developments and interest in platooning and higher levels of automation (SAE level 3+), the need for safety systems that are capable of monitoring system health and maintain safe operation in faulty scenarios is increasing. Methods for the detection, isolation and identification of faults in automated and cooperative driving is increasing. Once the existence of a fault is known, one needs to classify its severity and decide between fail-operational and fail-safe mitigation to guarantee the safety of a faulty vehicle.

The considered scenario in this research consists out of a vehicle suffering from a severe fault, such as a power steering or rear tire failure, whilst driving in an ACC string of vehicles on the right most lane of a highway. To accommodate failures in an automated vehicle, as a first contribution of this thesis a functional-safety architecture is proposed, which can enable safe operation in faulty scenarios. This architecture uses a nominal channel, a health monitor and a safety channel to incorporate all steps between nominal vehicle operation
and fault mitigation. To demonstrate the increase in safety potential of the first  contribution, its tactical decision making and fail-safe mitigation modules are implemented as a second contribution. The fail-safe mitigation uses an optimization-based algorithm to bring the faulty vehicle to a safe-state, being parked on the road shoulder. This maneuver is performed using nonlinear model predictive control (NMPC). To further highlight safety improvements of the functional-safety architecture, the prediction model of the NMPC is reconfigured. It uses the information from the fault detection and isolation module to optimize the tracking performance of the controller.

Assuming a string of ACC vehicles, results show different tactical decision making strategies the faulty-vehicle can perform to move to the road shoulder. The impact it has on the remainder of the string of vehicles shows a trade-off between stopping time and distance of the faulty vehicle and reconnection time for the remaining vehicles. Further results on the tracking performance of the NMPC show its robustness against severe faults and the increase in tracking performance when it uses the information from the proposed architecture. This highlights the safety improvement potential and need of both the functional-safety architecture and the fail-safe mitigation algorithm.

Supervisors

  • Laura Ferranti
  • Emilia Silvas (TNO)
  • Chris van der Ploeg (TNO)

Practically string stable, lateral control solution for a homogeneous platoon of vehicles: A Centralized vs Distributed MPC approach

Justin de Geus's MSc Thesis

Abstract

This thesis endeavors to develop a string stable, lateral controller for a homogeneous platoon of vehicles using a Model Predictive Control (MPC) approach. In the process two different control strategies tightly linked with the information flow topology, being centralized and distributed, are designed and compared in terms of reference tracking performance, noise- and disturbance rejection and practical implementation. Lastly, the developed controllers are simulated and validated using Siemens’ Simcenter Prescan software, after which the results are thoroughly discussed. Results have indicated that for the application discussed in this work, the centralized controller outperformed its competitor in the field of tracking performance and noise rejection, but not by a great margin. Furthermore, the novel developed definition of Practical Lateral String Stability (PLSS) guarantees stability for a platoon of n vehicles while using both controllers. To this end, the distributed controller is seen as worthy competitor and more workable solution due to the centralized controller’s issue of practical implementation. As part of anticipated future work, we plan testing the proposed approach with field experiments to validate the proposed method in real life.

Supervisors

  • Laura Ferranti
  • Joan Roca Nuñez (Siemens)
  • Mohsen Alirezeai (Siemens)

Privacy in Distributed Motion Planning

Jurriaan Gover's MSc Thesis

Abstract

Supervisors

  • Laura Ferranti
  • Javier Alonso-Mora

Active Safety Control for Semi-Autonomous Teleoperated Road Vehicle

Smit Jaman Saparia's MSc Thesis

Abstract

Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges like safe navigation in complex urban environments needs to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, a restriction on controller’s authority to intervene for lateral correction of the human operator’s commands is added to avoid counter-intuitive driving experience for the human operator. Further, this paper proposes visual feedback for the human operator to enhance trust over the system. In addition to this, the MPC’s prediction horizon data based unique predictive display is proposed to mitigate the effects of large latency in a teleoperation system. The performance of the proposed approach is tested in simulation using a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.

Supervisors

  • Laura Ferranti
  • Andreas Schimpe (TU Munich)
  • Prof. Dr.-Ing. M. Lienkamp (TU Munich)

Comfort Oriented Nonlinear Model Predictive Control for Autonomous Vehicles

Kuno van der Slot's MSc Thesis (2020)

Abstract

To promote automation in vehicles, autonomous driving should feel comfortable. Humans suffer for discomfort in certain frequencies in acceleration. By penalizing these frequencies a higher comfort performance can be achieved. Hence, we propose a comfort oriented nonlinear model predictive controller. The controller includes two band-pass filters to penalise the frequencies of motion sickness and general discomfort.

The controllers are tested on multiple scenarios, such as a double lane change and a sinusoidal trajectory. On the scenarios multiple disturbances are tested (e.g., wind disturbance and sensor noise).

We conclude that the MPC design that relies on the motion sickness filter has a significant decrease of motion sickness in scenarios where a lot of motion sickness is present, with improvements up to 30.7% compared to the basic controller. The MPC design that relies on the general discomfort filter helps bring the general discomfort down. On the double lane change maneuver with a changing velocity the general discomfort filter has improvements up to 10.7% compared to the basic controller.

Supervisors

  • Laura Ferranti
  • Riender Happee

Model Predictive Control for Automated Driving and Collision Avoidance

Nishant Chowdhri's MSc Thesis (2019)

Abstract

Supervisors

  • Barys Shyrokau
  • Laura Ferranti

Path Following Control Design for Passenger Comfort Under Disturbances

Shiyu Wan's MSc Thesis (2018)

Abstract

In recent years, enormous progress has been made in the field of automated driving. As a consequence, automated driving technologies are becoming increasingly popular. Research on comfort for autonomous vehicles, however, is still limited and unexplored. Some researchers address the comfort issue in path planning by velocity profiles, which regulates the instantaneous values of vehicle acceleration and jerk. Meanwhile, the actuator response to external
disturbances and the inaccurate following can result in the violation to the predesigned path, and therefore causes an uncomfortable driving experience. In order to tackle the passenger comfort issue from the perspective of path following control, this study proposes a frequency shaped model predictive control scheme that is (i) robust under external disturbances and (ii) able to optimize passenger comfort by regulating the vehicle lateral acceleration with respect to its corresponding frequency. The frequency is selected based on the comfort evaluation criteria proposed in ISO 2631. Further, the proposed controller is tested in three simulation scenarios, compared to three baseline controllers with respect to tracking accuracy and driving comfort. Finally, our analysis shows that the FSMPC controller can improve driving comfort, especially at the velocity higher than 60 km/h.

Supervisors

  • Riender Happee
  • Laura Ferranti

Fast MPC Solvers for Systems with Hard Real-time Constraints

Xi Zhang's MSc Thesis (2015-2016)

Abstract

We propose a primal-dual interior-point (PDIP) method for solving quadratic programming problems with linear inequality constraints that typically arise form MPC applications. We show that the solver converges (locally) quadratically to a suboptimal solution of the MPC problem. PDIP solvers rely on two phases: the damped and the pure Newton phases. Compared to state-of-the-art PDIP methods, our solver replaces the initial damped Newton phase (usually used to compute a medium-accuracy solution) with a dual solver based on Nesterov’s fast gradient scheme (DFG) that converges with a sublinear convergence rate of order O(1/k^2) to a medium-accuracy solution. The switching strategy to the pure Newton phase, compared to the state of the art, is computed in the dual space to exploit the dual information provided by the DFG in the first phase. Removing the damped Newton phase has the additional advantage that our solver saves the computational effort required by backtracking line search. The effectiveness of the proposed solver is demonstrated on a 2-dimensional discrete-time unstable system and on an aerospace application.

Supervisors

  • Tamas Keviczky
  • Laura Ferranti