Student Projects

Last update 16th October 2020
Note: we’re moving projects in from this page to ISA
For more information about the projects, send an email to the “contact” person.
For registration please contact the secretary of lab Mme. Bouendin.
For Internship + PDM in indusry, please contact Dr. Alireza Karimi.
Directives (2014) for projects at LA

Industrial projects are below


Semester & master projects (current)



United Technologies Research Center (UTRC) is the corporate research center of United Technologies Corporation (UTC), a dynamic global Fortune top 50 corporation operating at the leading edge of commercial and military aviation, aerospace systems, climate control, elevator design, and security and fire protection. UTRCI invites qualified individuals to apply for the following position in its Cork office. A competitive compensation and benefits package will be provided to the successful candidates.


The internship program will focus in the key areas of design and analysis for distributed control systems, with potential application on More Electric Aircraft (MEA).

The candidate will design and analyse distribute control systems for the advanced energy management of MEA equipment for the optimal management and sharing of available on-board electric power during overloading and failure conditions. The successful candidate should demonstrate strong knowledge of control systems design, distributed controls methodologies, and optimal control. Knowledge of supervisory energy management systems is desirable.

The intern will be based in the Control and Decision Support group where they will be part of a diverse, multinational team focused on innovation, working together in one of UTRC Ireland’s projects on aerospace control.

Required experience/skills

Enrolment in a PhD or Master’s degree in Electrical/Electronic Engineering, Mechanical Engineering, Computer Engineering, or a related field with strong background in Control, Optimization and System Identification.

Software skills in at least one of the following packages:MATLAB/Simulink, optimization software and tool with desired knowledge of C/C++ or Python.

This position will be located at UTRC – Ireland, Cork (Co. Cork) and will report to the in-country General Manager. To be eligible to apply for the position, candidates must be legally entitled to work and reside in Ireland.

UTRC Contact: Marcello Torchio [email protected]

EPFL Contact: Prof Giancarlo Ferrari Trecate [email protected]

The LA recently upgraded its single arm inverted pendulum into a triple arm inverted pendulum. The mechanical part as well as the measurements part are now completed. The fun part can begging. The challenge of this project is to control the triple inverted pendulum. Control scenarios will first be simulated using mathematica and/or matlab The implemented and validated using LabVIEW.

This project requires the following courses: Control Systems or Automatic and Commande Multivariable or equivalent. Nonlinear control will be a plus.

Teacher: Philippe Mullhaupt and Christoph Salzmann

Type of project: Semester or Master

Contact:  Philippe Mullhaupt

Industrial projects (current)

Batch distillation is a common operation used in the chemical industry for the separation of liquid mixtures. The driving force for separation lies in the differences in relative volatilities of the various components. A key variable in distillation is the composition at the top of the column. Since real-time composition measurements are not common in practice, it is usually necessary to infer that information from other, more easily available measurements such as temperatures and pressure. This project aims at developing a dynamic observer such as a Luenberger observer or a Kalman filter to estimate composition on the basis of physical measurements such as temperature and pressure.

Based on an existing dynamic model, the student will first design the observer and test it in simulation. Then, the observer will be used, in simulation, for the optimization of an industrial batch distillation column, using measurement-based optimization techniques developed at the Laboratoire d’Automatique.

We are looking for a master student with a good background in process systems engineering and control as well as a working knowledge of Matlab/Simulink.

The project can be organized as a regular master project at EPFL or a master project in industry with a 25-week internship at Online Control under the guidance of the Laboratoire d’Automatique. The project can be started right away or later in 2020. Interested students should send their CV and a copy of their academic record to:                     

     Prof. Dominique Bonvin                               

     E-mail: [email protected]               

     Mobile:  +41 79 544 4667                             

 Academic coordinator: Dr. Alireza Karimi

Distillation is a separation process widely used in the chemical and petrochemical industries because of its flexibility for separating a mixture into components of different relative volatilities. Ideally, a continuous distillation column operates at constant operating conditions. However, due to changes in feed conditions and disturbances, the optimal operating conditions change with time. This project aims at developing and testing in simulation a real-time optimization strategy that is capable of optimizing the separation, while enforcing safety and quality constraints.

Based on an existing dynamic model for a rectifying column, the student will first extend it to a stripping-rectifying column and then test in simulation various strategies to control the pressure and the temperature at the top of the columnand the gas flow through the column. Optimal operation will then be achieved by generating optimal controller setpoints using a measurement-based optimization strategy developed at the Laboratoire d’Automatique.

We are looking for a master student with a good background in process systems engineering and control as well as a working knowledge of Matlab/Simulink.

The project can be organized as a regular master project at EPFL or a master project in industry with a 25-week internship at Online Control under the guidance of the Laboratoire d’Automatique. The project can be started right away or later in 2020. Interested students should send their CV and a copy of their academic record to:

       Prof. Dominique Bonvin

       E-mail: [email protected]

       Mobile:  +41 79 544 4667                

Academic coordinator: Dr. Alireza Karimi

To address climate change properly and accelerate energy transition from fossil fuels to renewable energy, new energy generation technologies are required. They have to be reliable, efficient, scalable and provide sufficient low-cost energy to satisfy current and future demands. Airborne Wind Energy (AWE) is an emerging technology that involves autonomous tethered aircraft to produce electricity from high altitude winds. 
Skypull can offer a solution and proposes an innovative approach, based on a new revolutionary type of flight device, aimed at maximizing the lift, with minimum resistance increase and allowing autonomous operation: a “box” drone with proles multi-element aerodynamics, characterized by high aerodynamic performance, intrinsic structural strength, low weight and low production costs. 
In order to keep the drone operational over prolonged periods of time it is critical to recharge the onboard electronics during operation. Skypull developed the technology of regenerative differential braking, which allows the controlled braking of the aircraft while simultaneously regenerating energy. 


In order to better understand the process of differential braking our drone model needs to be extended to accommodate for the braking process. The models focus will be on the drivetrain (motor controller, motor and propeller), but should be integrated in the drone model. 

Based on this model a set of control laws will be developed to enable the drone to brake, turn and regenerate energy in a stable and robust way.


* Literatue/Skypull technology review 
* Installation of a test bench 
* Implementation of the drive train model and integration into drone model 
* Derivation of control strategy for differential braking 
* Testing on Skypull hardware


* Good understanding of Simulink / MATLAB 
* Good fundamental knowledge in control system design and modelling 
* Basic knowledge of aerodynamics and aircraft actuators 
* Basic knowledge of motor controllers 
* Ability to work independently, proactive personality and highly motivated 

Professor:Colin Jones

Type of Project: Master in Industry

Industrial contacts 

Andrea Pedrioli Simulation Engineer, [email protected]

Aldo Cattano Chief Technology Officer, [email protected] 
Skypull SA, via alla Stampa 49, 6967 Lugano


* Loyd M. L., “Crosswind kite power”, Journal of Energy 4(3) 106-111 (1980). 
* Kathryn E. Johnson , “Adaptive Torque Control of Variable Speed Wind Turbines”

The energy systems group at CSEMis developing innovative technologies at the intersection of power and energy, control, and data science. In the march towards a fully- decarbonised energy system, we are developing control solutions to make buildings and grids more energy-efficient and more flexible. The current state of the art is model- predictive control to optimize the operating conditions of distributed energy resources. However its practical deployment is hindered by the time and effort required to build models of these resources, and by the inability of most models to reflect the impact of occupants on the energy performance of buildings.

In this context, we are offering an internship on the development of artificial-intelligence algorithms. The objective of the project is to build on state-of-the-art algorithms in deep reinforcement learning to create robust and optimal control strategies. Such algorithms include deep Q-networks (DQN, first demonstrated by Deepmindin 2013 to play Atari games), deep deterministic policy gradient (DDPG, first introduced by Google Deepmindin 2015 for simple motion control tasks), and proximal policy approximation (PPO, introduced by Open AI in 2017 for humanoid running and steering).

The physical nature of the control problems in energy management raises some specific challenges for these algorithms, especially in terms of convergence. The project will therefore combine theoretical aspects, with an opportunity to improve some of the most advanced general-purpose artificial intelligence algorithms, and practical aspects involving the rapid prototyping of software architectures and the implementation of algorithms.

While the objective of the work is clearly industrial, its nature means there will be scope to present the results in scientific journals or at conferences. The student will be part of a team of twelve experienced engineers and researchers.

Professor:Colin Jones

Type of Project: Master in Industry

Contacts: Pierre-Jean Alet, Baptiste Schubnel

This project is proposed by a swiss company active in the field of 3D printing and converging digital manufacturing dedicating its efforts to offer high-end 3D printing instruments and solutions.

Combining classical 3D printing technologies (i.e. extrusion of a large variety of materials) with emerging advanced fabrication approaches enables new ways of tuning construct properties. Among others these can be mechanical strength and anisotropy or construct functionality, such as electrical conductivity, chemical bonding and even biological functions.

One advanced fabrication approach is the production of micro fibres using electro hydrodynamic dispensing. This technology allows the production of micro-fibres on the scale of several microns diameter with an approximately constant extrusion speed.

The goal of this project is to optimize the print-head (robot) control in order to increase the fibre deposition quality. Different approaches can be envisaged combining linear system identification with iterative learning control methods. The key topics are: understanding the relationship of fibre production and robot dynamics, choice of appropriate control methodology and proof-of-concept, implementation and testing.

MER: Alireza Karimi

Type of project: Master in industry

Contact: Alireza Karimi

Autonomous driving technologies that aim for more safety, comfort, and environmentally friendly vehicles, have been growing rapidly in automotive industry recently. These systems go beyond vehicle-centric assistant applications by integrating traffic and environment information. One of the main challenges in autonomous driving development is to design and then validate the planning and control algorithms in a closed-loop fashion, where both vehicle dynamics characteristics and a wide variety of traffic scenarios are taken into account. The designs should also guarantee optimal performance toward precise tracking, and time/fuel optimality with respect to both vehicle and traffic constraints (i.e. avoid pedestrians and other cars).

One of the main innovation objectives of Siemens PLM is to accelerate the research and development activities of ADAS (advanced driver assistance systems) and autonomous driving technologies. We are continuously looking for outstanding students who are eager to do their Master thesis or internship on a challenging research project in a highly dynamic and international research environment. We have a variety of possible projects available that cover different aspects of planning and control algorithms, ranging from very theoretical to practical. The company also provides various tools to support the research activities, for example, Imagine.Lab Amesim for vehicle dynamics modelling, PreScan for sensor (camera, lidar, radar,…) and traffic environment modelling, and a miniature race car setup for embedded control implementation.

Examples of possible (but not limited to) topics: optimal control and learning control for an ADAS application (i.e. roundabout crossing, valet parking), fast embedded optimal control algorithm, optimal path/motion planning, simultaneous localization and mapping (SLAM) in virtual environment, vehicle dynamics, traffic planning and verification…

Background: control systems, robotics, computer science, or mathematics, familiar with programming. Experience with optimization, vehicle dynamics, ROS or autonomous vehicles is a plus.

Professor: Colin Jones

Type of project: Master

Contact: Colin Jones or Dr. Son Tong

Image result for siemens logo The work will be performed in collaboration with Siemens PLM Software in Leuven, Belgium.

Houses are commonly equipped with PV panels, electric batteries, variable power heat pump and electric vehicles. This means single homes are becoming complex energy hubs which require an efficient energy management system (EMS) that can be configured in a simple way while satisfying complex needs such as improving self-consumption and reducing cost.

The energy systems group at CSEM is actively developing a next-generation energy management system that is based on Model Predictive Control and which can be easily reconfigured through an automated controller synthesis process. A first internship successfully contributed to this project with work focused on forecasting. In this context, we are offering an internship to improve the operation of this EMS by extending its functionalities, focusing on two aspects:

  • The refinement of the predictive control algorithm with specification of complex objective such as peak shaving in the EMS algorithm, and the scale-up to multi-home systems.
  • The buildup of a simulation environment that allows to testbench the controller. The simulation will rely on the versatile physical simulation environment Modelica, for which a collection of house models has already been developed. It will be used to assess the controller performance over long periods of time.

While the objective of the work is clearly industrial, there will be scope to present the results in scientific journals or at conferences. The student will be part of a team of twelve experienced engineers and researchers, and have the opportunity to use real data from test sites and face actual practical challenges of software deployment for energy applications.


  • A strong interest for energy systems and energy management problematics.
  • Good programming experience, preferably with exposure to Python; and an interest in good coding practices (object-oriented programming, unit testing, version tracking with Git).
  • Familiarity with MPC or numerical optimization preferred, knowledge of Kalman filtering and/or machine learning a plus.

Type of project: Master in industry or internship, minimum 4 months.

Location: Neuchatel

Contact: Tomasz Gorecki, [email protected]

Professor: Colin Jones, [email protected]

Past projects (not yet ready)

Projects 2018

Projects 2017

Projects 2016

Projects 2015

Projects 2014

Projects 2013

Projects 2012