Student Projects

Last update 5th December 2019
 
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)

 

Description:

Actuator/sensor placement is a combinatorial optimization problem, where one assigns controllers/sensors in a large-scale network, like robotic network and power system. Since no computationally feasible solution exists (sometimes one can even prove the NP hardness), an approximate solution with performance guarantee is acceptable. One surprising finding in this research area is that one can embed this optimisation problem (non-chronological) into the approximate dynamic programming framework (chronological). The key to this method is to find a heuristic that selects controllers one by one, while from literature one can find tons of such heuristics.

In this project, the student should apply the method mentioned above, analyze the advantage and explore some other variants. The abundance in DP/RL research could add more possibilities for its application in combinatorial optimization problems. Other possibilities regarding the same topic include continuous extension and adding stochasticity to the exploration.

Type of Project: Semester

Professor: Giancarlo Ferrari Trecate

Contact: Baiwei Guo

Description:

Microgrids (mGs) are small-scale electric networks comprising different devices such as distributed generation units (DGUs) interfaced with power-electronic converters, energy storage units, and loads. MGs can operate in grid-connected and islanded modes, and are compatible with both AC and DC operating paradigms. In particular, DC microgrids (DCmGs), due to their ability to interface naturally with several renewable energy sources (for instance PV modules), batteries, and electronic loads (various appliances, LEDs, electric vehicles, etc), have gained traction in the recent years.

Despite these advantages, mGs present several stability- and performance- related challenges. At a local (primary) level, mG technology should allow DGUs and loads to join and leave a DCmG, and simultaneously ensure system stability with minimal human intervention. Similarly, at a higher level, a supervisory control structure must provide necessary commands to the primary level such that a well scheduled and balanced utilisation of various resources is achieved. 

For this project, we consider a supervisory secondary control layer resting atop a voltage-controlled primary layer, which receives power references from an energy management system. The secondary controller translates these power references into appropriate voltage references by utilising the admittance matrix of the DCmG network. However, an a-priori knowledge of this matrix is not straightforward as its estimation requires not only the topology of the DCmG network but also the parameters of individual power lines. Tellingly, this can be prohibitive for large mGs, whose topologies change with the addition/removal of DGUs and loads. The goal of this project is to leverage data-driven methods and machine learning (ML) to determine the topology and the admittance matrix of the DCmG. Furthermore, we intend to analyse the performance of the secondary controller interfaced with the estimated matrix. 

Requirements: Basic course in control theory and linear algebra, basic experience in programming. Familiarity with Matlab or Python, mGs, and ML is a plus.

Tentative project plan:

  • Reading relevant literature and familiarising with the subject
  • Simplified estimation of the admittance matrix under complete observability 
    • Detailed simulation studies to study the validity of approach
  • Interfacing the estimated admittance matrix with the secondary controller
  • Performance of the secondary controller with the the estimated admittance matrix

Professor: Giancarlo Ferrari Trecate

Type of Project: Semester or Master

Supervisor: Pulkit Nahata

Description:

In the past decades, there has been a paradigm shift in the operation of power systems with an increased focus on localized and distributed generation as opposed to centralised mechanisms. Microgrids (mGs), both AC and DC, are spatially distributed systems composed of multiple small subsystems, for example, flexible loads, distributed small-scale generation, storage units etc., interconnected to each other through an electrical network. The manifold advantages of mGs like enhanced power quality, reduced transmission losses, capability to operate in grid-connected and islanded modes, and compatibility with renewable distributed generation, make them a promising operational architecture for future power systems. Moreover, recent series of legislation, strongly favouring the concept of sustainable and renewable energy, has reinvigorated interest in stable operation and control of mGs.

The overall control of an islanded DC microgrid (DCmG) is a multi-objective problem spanning different control stages, time scales, and physical layers. For a stable and economic operation of a microgrid, a hierarchical control scheme comprising primary, secodary and tertiary control layers is often employed. In order to study the coordinated operation of these layers, a modelling framework is of paramount importance. The goal of this project is to ease simulation and enable fast prototyping of AC and DC mGs interfaced with hierarchical controllers. Some basic components are present in simulation environments for electrical systems such as MATLAB Simulink , which not only allow one to build custom libraries of blocks for facilitating the modeling of specific electric systems, but also present a graphical user interface. However, these are general-purpose software packages, and building realistic models of large mGs can be time consuming. In this project, we will develop libraries collecting models of DGUs that are commonly found in AC and DC mGs. The blocks for controllers that will be developed as part of this project will be coded from scratch, and will eventually be made available publicly.

Requirements: No specific prior requirements. Familiarity with MATLAB Simulink, and mGs is a plus.

Professor: Giancarlo Ferrari Trecate

Type of Project: Semester 

Supervisor: Pulkit Nahata

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.

Description:

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]

Description:

Deep networks have become invaluable tools for supervised machine learning, e.g., they been successfully applied in many computer vision and classification tasks. While often offering superior results over traditional techniques, deep architectures are known to be challenging to design and train such that they generalize well to new data. Important issues with deep architectures are numerical instabilities in learning algorithms commonly called exploding or vanishing gradients.

The idea of this project is to use a new type of networks based on a system of Ordinary Differential Equations (ODE): Hamiltonian inspired Neural Networks. Hamiltonian systems are attractive due to their energy-preservation properties which can result in more stable deep networks.

In this project, the student will develop and test Hamiltonian networks over simple classification problems and then move towards more complex dataset (e.g. CIFAR-10 or CIFAR-100) where other types of layers may be added.  Matlab and GPU-based implementations of the associated training algorithms are also anticipated.

Requirements: System and Control Theory, Machine Learning, experience with Matlab.

Professor: Giancarlo Ferrari Trecate

Type of Project: Semester or Master

Supervisor: Clara Galimberti

Description:
In the power electronics domain, controller design is rarely carried out fully in a model-based fashion. Although hand tuning gains can lead to good performance in many cases, this process might become infeasible when many parameters have to be adjusted simultaneously.
 
In this project we plan to design a mathematical model for a real photovoltaic water pumping system currently deployed in Guinea-Bissau and Jamaica. This model will comprise the photovoltaic panels, two power electronic stages, and a water pump. Next we will proceed to model simplification and controller design, with the aim of enhancing its dynamic response.
 
Requirements:
Good circuit analysis skills, familiarity with systems and control modelling concepts. Knowledge of power electronics, nonlinear dynamics or MPC is a plus. 
 
Professor:
Colin Jones
 
Type of project:
Semester or Master
 
Contact:
Emilio Maddalena
 
Description:
Obtaining a complete and detailed dynamic model of office buildings composed of many thermal zones is a challenging task. This is one of the main motives why Model Predictive Control is not adopted by the HVAC industry, even though it is known to lead to lower energy consumption patterns when compared to rule-based or PID controllers. Adaptive MPC schemes seem therefore to be the best solution in the building context.
 
In this project we plan to ponder about the following practical questions:
1 – Is it possible to start with only a rough mathematical description of the building and refine it during operation, while satisfying constrains?
2 – How to efficiently train a predictive controller with the data being constantly generated by buildings?
 
Requirements:
Good understanding of MPC and system identification. Machine learning and optimization are a plus.
 
Professor:
Colin Jones
 
Type of project:
Semester or Master
 
Contact:
Emilio Maddalena, Yingzhao Lian
 

The EPFL babyfoot is under continuous improvement, see (https://www.epfl.ch/labs/la/studentprojects/babyfoot/) 

The actual dynamic for the translation is slow to catch ball shot with an angle. This project aims at predicting the ball trajectoire using the opponent measurements and ball position to compensate for the slow dynamic.

Students suggested improvements are also welcome.

Requirements: LabVIEW

Type of project: Semester 

Contact:  Christophe Salzmann

 

Description:
Microgrids (mGs) are low voltage energy networks comprising small distributed generation units (DGUs) and loads. Their development has been motivated by their  many advantages such as facilitating the use of renewable resources, bringing generation close to loads, and increasing the robustness of the electric system by increasing the redundancy in generation.

Despite these advantages, mGs present several stability- and performance-related challenges. At local level, mG technology should allow DGUs and loads to join and leave a mG, and simultaneously ensure system stability with minimal supervision efforts. Similarly, at a higher level, multiple mGs should be able to join and split seamlessly, so that flexible coalitions can be realised. Some of these goals can be achieved in a completely decentralised fashion, however, a few require centralised or distributed supervisory control. 

Data-driven controllers facilitate the synthesis of controllers directly from the data of the system without the need for a mathematical model. This is of great importance for systems with inaccurate, time-changing, or difficult-to-obtain model information. For mGs, data-driven controllers can potentially eliminate the need for model knowledge for synthesising stabilising controllers, allowing for flexible and adaptive operation. Previous projects have applied reinforcement learning (RL), a data-driven control method that has gained substantial popularity in recent years, for the centralised supervisory control of mGs. The goal of this project is to explore and assess distributed RL methods for distributed supervisory control of mGs. The project is expected to be relatively applied, without much emphasis on proofs of stability for proposed control algorithms.

Requirements: Basic course in control theory and linear algebra, basic experience in programming. Familiarity with Python, mGs, and RL is a plus.

Professor: Giancarlo Ferrari Trecate

Type of Project: Semester or Master

Supervisor: Mustafa Sahin Turan

Control and Identification of Hovercraft

Controlling underactuated system is always a challenging task even though it finds common applications in real life, such as rockets, boats and fix-wing airplanes. This project aims at finding a reliable control solution of hovercraft, a light weight underactuated mechatronic system. This project could be carried out in the following direction:
  1. Automated tuning of back-stepping controller based on Bayesian optimization (master project)
  2. Online Bayesian parameter identification. (semester project)
  3. Distributed control and formation keeping (semester project)
  4. MPC based path plannining (semester project)
  5. Passivity based trajectory tracking. (master project/semester project)
  6. Barrier function based robust control(master project)

A basic experimental setup, a simulator and a back-stepping controller is now available and we expect all the students to implement their algorithm in experiments. Therefore the aforementioned projects require a good knowledge of ROS, Matlab and either python or C++. Meanwhile, a good command of convex optimization and Bayesian computation will be plus.

Professor : Colin Jones

Type of project: Semester / Master

Contact: Yingzhao Lian

Optimal control of a hydrofoil boat

HYDROS, the Swiss scientific research center specialized in sailing, is holding an engineering competition to take place in lake Léman, summer of 2017 (see contestpromo video). The goal is the construction of an energy efficient hydrofoil boat. EPFL has forged a group of teams that specialize on optimizing each of the specific components of the system. LA is responsible for the dynamical modeling of the boat in order to plan effective trajectories and track them in the face of uncertainties.

We’re looking to build a team of students with diverse backgrounds, including robotics, mechatronic systems, electronics and computer science. The main goal is to develop advanced autonomous flight control and pilot-assistance systems. Various topics include:

  • Identification: The dynamics of a boat with hydrodynamic support is inherently nonlinear and very complex due to numerous hydrodynamic effects. Having a reliable mathematical model is crucial for control algorithms design and flight simulation. We, therefore, would start with designing experiments and collecting flight data during piloted tests. The dynamic optimisation approach will be then employed to estimate hydrodynamic coefficients of the foil.
  • State Estimation: The current flight control algorithm only makes use of roll, pitch rates and height information. In a fully autonomous mode accurate estimation of the longitudinal and lateral velocities, as well as position is very important. For that reason, the Multiplicative (Quaternion-based) Extended Kalman algorithm and a generic embeddable implementation in C++ were developed. The goal is to continue the integration of the framework in the boat control system and extend the existing code with new numerical techniques (Unscented KF, robust predictors and so on).
  • Optimal Control: The goal is to design and implement a pilot-assisting optimal control algorithm. At first, the performance of the Linear Quadratic Regulator (LQR) should be analysed on the real boat. If time will allow, a real-time implementation Nonlinear Model Predictive Control algorithm based on the toolbox developed in the lab will be tested in simulation and in a flight test.

Requirements: System and Control Theory, experience with C++ and Matlab (or Python), experience with the PixHawk firmware. Knowledge of ROS and mathematical optimisation will be a big plus.

Professor : Colin Jones

Type of project: Semester / Master

Contact:   Petr Listov 

Wind resources tend to be significantly stronger and more consistent with increasing altitude. Furthermore, conventional wind turbines quickly reach their limit in accessing these higher winds due to structural limitations. This creates a potential for improved power generation with Airborne Wind Energy systems. A frequent design for such systems includes a flying airfoil tethered to a ground station. The station is equipped with a power generator and the aerodynamic force generated by the flying wing is used to drive this generator as the tether reels out under the aerodynamic force. Once the tether reaches its maximum length the wing enters a low force generating gliding trajectory and the tether is reeled back in with low power consumption, thus generating a net positive power generation cycle.

The execution of such motion, however, requires overcoming critical control challenges in presence of system and environmental (wind) uncertainties. The proposed project will provide opportunities for students to work on different aspects of the system and has several sub projects as listed below:

  • Small-scale (indoor) prototype: Validation of the novel control and estimation techniques on outdoors requires particular wind conditions, coordination with local authorities and often due to possible prototype failure. Many of these problems can be circumvented by flying a scaled version indoors. The current indoor prototype has a number of limitations such as onboard processing power and communications bandwidth. The goal of this project is to choose an appropriate computation platform (subject to strict weight and size constraints) and a communication chip (device). Design and 3D print a mount for the hardware and perform a manual/[or autonomous] flight test.
  • Outdoor prototype: The outdoor prototype comprises the ground station [developed] and a flying vehicle. The ground station is an autonomously operating system that should be able to measure spherical coordinates of a kite and tether force, send control signals and receive/log sensor and telemetry data from a kite. The goal of this project is to build a kite based on a commercial foam glider (e.g. EasyGlider 4), choose and mount an appropriate onboard computer and a communication device, as well as necessary avionics and perform a piloted/[or if time allows autonomous] flight.

Requirements: Experience with CAD systems, Embedded Programming [C/C++ or Embedded Python]. Knowledge of flight mechanics and ROS is a plus.

Professor : Colin Jones

Type of project: Semester / Master

Contact:   Petr Listov 

The unpredictable pattern of distributed carbon-free renewable sources poses challenges to modern power systems. Meanwhile, commercial buildings, which represent over 40% of EU energy consumption, reveal their potential in resolving this challenge. On one side, a properly controlled building can adjust its energy consumption to meet the variable generation, a strategy called demand response (DR). On the other hand, modeling and controlling the building could significantly lower the total energy consumption.

On our EPFL campus, a modern mechanical engineering building (MED) equipped with informative sensors and high performance actuators has come into service since 2016. Its four-layer, multi-zone and multi-use structure replicates most the commercial building control schemes in practice, enabling us to explore various research questions and application. The proposed project motivates students to utilize the years of available data in order to:

  1. Create a reliable and interpretable multi-zone model of the temperature dynamics .including the effects of internal and external disturbances;
  2. Use machine learning techniques to predict its energy consumption based on historical data and weather forecasts;
  3. Semi-supervised learning based modelling with incomplete data.

We currently have a unified database interface, as well as a general framework of modelling and data analysis.

Requirements: Clean and tidy coding habits and passion; a good knowledge of statistics, optimization and machine learning.

Professor: Colin Jones

Type of project: Semester or Master

Contact: Emilio Maddalena, YingzhaoLian

The constant rise in electricity demand in emerging nations leads to frequent load shedding and blackouts, causing unexpected inconvenience to their citizens. Since last year, Pakistan has achieved an average generation vs. demand balance, but still faces network instability due to the insufficient tolerance to demand peaks. Besides the long-term projects in upgrading the transmission system, demand response turns out to be a proper plan for alleviating or even resolving this instability. More specifically, controllable loads modify their electricity consumption pattern to meet the performance of grid, thus clipping the load peaks. Demand response has recently been a hot topic in power systems over the last decades because of the emerging distributed generation. This project aims at exploiting its potential beyond the modern power systems, we hereby conclude the all the intriguing aspects as follows:

  1. Understand/ investigate Pakistan’s power system and explore possible application scenarios of demand response. Compare it with the modern power system, in particular Swiss grid.
  2. System identification or parameter estimation of the network models.
  3. Demand response incentive MPC, specifically with a controllable shopping mall, allowing for the networks limits and the customers’ comfort level, etc. This direction relates more to quasi real-time dynamics optimization problem.
  4. Scheduling the power consumption sequence, allocating corresponding demand respond task accordingly. This direction relates more to large scale slow dynamics optimization problem.
  5. Static planning of the power system infrastructure, such as battery allocation and PV installation. This direction relates more to static optimization problems.

The specific problem to be tackled will be decided according to the background of the student and his/her interests. This project gives students chances to have better understanding of both modern and developing grids, and it is also a rare chance to touch real-world data as well as a real-world large problem.

Requirements:basic knowledge of power systems, MPC, system identification, good coding habit.

Professor:Colin Jones

Type of project: Semester or Master

Contact: Emilio Maddalena, YingzhaoLian

In this project, the student develop a simulation model for a flexible joint. Then based on simulation data a nonlinear controller using the reinforcement learning algorithm is designed. This controller will be compared with a linear quadratic controller in simulation and on the real system.

Domaine(s) d’activité: Automatique et mécatronique

Responsable : Karimi Alireza

In the past decades, there has been a paradigm shift in the operation of power systems with an increased focus on localized and distributed generation as opposed to centralised mechanisms. Microgrids (mGs), both AC and DC, are spatially distributed systems composed of multiple small subsystems, for example, fexible loads, distributed small-scale generation, storage units etc., interconnected to each other through an electrical network. The manifold advantages of mGs like enhanced power quality, reduced transmission losses, capability to operate in grid-connected and islanded modes, and compatibility with renewable distributed generation, make them a promising operational architecture for future power systems. Moreover, recent series of legislation, strongly favouring the concept of sustainable and renewable energy, has reinvigorated interest in stable operation and control of mGs.

In order to study mGs and successively apply efficient control algorithms, a modelling framework is of paramount importance. The goal of this project is to ease simulation and enable fast prototyping of AC and DC mGs. Some basic components are present in simulation environments for electrical systems such as PSCAD, which not only allow one to build custom libraries of blocks for facilitating the modeling of specific electric systems, but also present a graphical user interface. However, these are general-purpose software packages, and building realistic models of large mGs can be time consuming. In this project, we will develop libraries collecting models of DGUs that are commonly found in AC and DC mGs. PSCAD is chosen as the software environment for mG simulation. Theblocks for controllers that will be developed as part of this project will be coded from scratch, and will eventually be made available publicly.

Requirements: No specific prior requirements. Familiarity with PSCAD, and mGs is a plus.

Professor: Giancarlo Ferrari Trecate

Type of project: Semester or Master

Contact: Pulkit Nahata

Microgrids (mGs) are low voltage energy networks comprising small DGUs and loads. Their development has been motivated by several reasons. First, they foster the use of renewable resources, in line with decarbonization targets set by several countries. Second, they bring generation close to loads hence avoiding transmission of electric power over long distances. Third, redundancy in generation can increase the robustness of the electric system. At the same time it can significantly improve power quality. On the economical side, mGs are seen as the key component of agile power systems making them one of the most promising emerging technologies. Moreover, mGs allow two-way flow of power and are expected to contribute to the deregulation of the energy market by allowing the active participation of consumers and owners of small generation units.

Despite their manifold advantages, mGs present several stability- and performance-related issues. At local level, mG technology should allow DGUs and loads to join and leave a mG, and simultaneously ensure system stability with minimal supervision efforts. Similarly, at a higher level, multiple mGs should be able to join and split seamlessly, so that flexible coalitions can be realised. Certain goals can be achieved in a distributed or decentralised fashion, however, a few require centralised supervisory control. Examples are faults in the loads, which can propagate and destabilize the system, and cyberattacks, which target the control communication network for tampering with the mG dynamics. Therefore, local controllers need to be complemented with algorithms for detecting malfunctions in the system and possibly trigger remedial actions such as control reconfiguration. The aim of this project is realise a modular supervisory control architecture initially in a centralised fashion and later explore the feasibility of a distributed/decentralised architecture. The project is expected to have a theoretical flavour including analytical proofs followed by validation through necessary simulations.

Requirements: Basic course in control theory, linear algebra, and sufficient mathematical maturity. Familiarity with Matlab/Simulink, PSCAD, and mGs is a plus.

Professor: Giancarlo Ferrari Trecate

Type of project: Semester or Master

Contact: Pulkit Nahata

Wind resources tend to be significantly stronger and more consistent with increasing altitude. Furthermore conventional wind turbines quickly reach their limit in accessing these higher winds due to structural limitations. This creates a potential for improved power generation with Airborne Wind Energy systems. A frequent design for such systems includes a flying airfoil tethered to a ground station. The station is equipped with a power generator and the aerodynamic force generated by the flying wing is used to drive this generator as the tether reels out under the aerodynamic force. Once the tether reaches its maximum length the wing enters a low force generating gliding trajectory and the tether is reeled back in with low power consumption, thus generating a net positive power generation cycle.

The execution of such motion however requires overcoming critical control challenges and perform control and optimization for improved power generation in presence of system and environmental (wind) uncertainties. The proposed project will provide opportunities to students to work on different aspects of the system and has several subprojects as listed below:

  1. System modeling and nonlinear control:Will explore methods for modeling the system for better control and will explore the tools of robust and adaptive nonlinear control theory to implement controllers for the existing experimental platforms.
  2. Path following model predictive control: Will implement and test model predictive control (MPC) schemes for tracking of power optimal trajectories in the experimental system.
  3. Data based modeling and control of experimental systems: We will explore the use of Gaussian Process Machine Learning tools to model the dynamics and performance characteristics of the system to find the optimal control inputs for the system. The learning based models further provide an improved prediction performance crucial for the model based control methods explored above.
  4. Design of the Ground Control Unit: we will improve mechanical and electrical design of the existing small scale ground station prototype. The ground station is an autonomously operating system that should be able to measure spherical coordinates of a kite and tether force, send control signals and receive/log sensor and telemetry data from kite.

Requirements: Experience with CAD systems, Embedded Programming ( C/C++ or Embedded Python), knowledge of ROS is a plus.

Realistic high fidelity simulators and working experimental platforms are ready and will be available to bench test the controllers and learning algorithms on the real system.

Professor: Colin Jones

Type of project: Semester or Master

Contact: Sanket DiwalePetr Listov 

The train system is one the most important large-scale infrastructures of a country. In recent decades, renovating the system by adding locomotives equipped with high-power/high-performance power electronic converters has emerged new challenges. One of these challenges is the increase of the magnitude of some harmonics in the electrical systems, which in critical circumstances can lead to failing of the system. Generally, in this type of converters, the input impedance of the converter can be active in the frequencies higher than the synchronous frequency of the system. Consequently, some harmonics are amplified by the converter. In order to resolve this problem, the output impedance of the converter for these harmonics should be passive (the argument of the impedance should be between -π/2 and π/2) which can be realized by means of a well-designed control system.

The objective of this project is to design a controller for the grid-side power electronic converter of a traction system in order to have passive input impedance for a wide range of the critical harmonics of the system. A robust fixed-structure controller design method will be used to achieve this goal.  In the first step of this project, the frequency response and behavior of the grid-side power electronic converter with emphasis on resonance frequencies will be studies. In the second step, an appropriate mathematical model for converter will be derived. This mathematical representation of the plant should be compatible with the controller design approach. In the third step, the controller will be designed by means of the above-mentioned method. In the fourth step, the performance of the designed controller will be validated through simulation and finally, at the fifth step, the proposed method will be validated by experimental results.

Requirement: Background in power electronics and control theory.

MER: Alireza Karimi

Type of project: Master

Contact: Seyed Sohail Madani

Adaptive optics is a technology by which the dynamic evolution of the aberrations of an optical system are corrected. This typically involves a wave front sensor, a fast real-time computer and a deformable mirror. This technology is mainly applied in astronomy to compensate for the effects of atmospheric turbulence and ophthalmology for precise imaging of the retinal tissues.

The department of astronomy of the University of Geneva is developing a sophisticated Infra-Red spectrograph for the discovery and characterization of exo-planets named NIRPS. This spectrograph benefits from an adaptive optics system featuring a deformable mirror with 241 actuators (Degrees of freedom) that will be updated at 1000Hz. Presently, traditional control methods are used to compensate for the turbulence, based on auto-tuned PID.

As with all adaptive systems operated on real operational telescope, it is expected that spurious vibrations will be present once the system is installed on the telescope. The objective of this project is to design an advanced controller that identifies and reject the vibrations. The state-of-the-art methods for vibration control in adaptive optics should be studied and an appropriate controller should be designed and implemented on the real instrument NIRPS.

This project is carried out in collaboration with the Observatory of Geneva, which is is the department of Astronomy of the University of Geneva. Its exo-planet research group is one of the world’s most renowned and performing group. It is the group that has discovered the world’s first exo-planet (1995, M. Mayor, D. Queloz). It has designed and operates the world’s most powerful exo-planet discovery and characterization machines (HARPS) and HARPS-N. Besides astronomers, the group includes specialists is optics, control systems, interferometry, electronics and software. It has had a major participation in the extreme adaptive optics instrument SPHERE and it is currently leading the development of the Adaptive Optics based instrument NIRPS.

MER: Alireza Karimi

Type of project: Master

Contact: Alireza Karimi, Francois Wildi

Blending of raw materials to achieve a desired product quality continuously or for a batch (e.g. container, silo, pile) is important in many industrial domains such as food, beverage, mining, chemical, energy, and cement. If the raw material compositions vary significantly, a fixed recipe for the raw material feeder proportions cannot be applied anymore. Instead, the raw material proportions are typically adjusted automatically based on the composition of the blended product measured on-line or at-line. Typically, raw materials with low quality variability (e.g., high grades, correctives) are used to correct quality changes of bulk raw materials with large variability (e.g., low grades, from quarries or mines).

In this project, two applications in the cement industry and coal-fired power stations are studied using a MIMO controller: (i) in raw mills of the cement industry, the manipulated variables are the feeder flowrates of the raw materials (e.g. limestone, clay, sand, iron ore), the controlled variables are nonlinear functions of the product oxides, and the measurement device is an at-line XRF spectrometer with significant transport and measurement delay. (ii) In coal stockyards of power stations, the manipulated variables are the reclaimer flowrates of the various subpiles with high-, low and normal-grade coals, the controlled variable is e.g. the calorific value with an upper limit on a selected oxide, and the measurement device is an on-line PGNA analyzer spectrometer with variable transport delay.

Recently, a new robust controller design using frequency-domain data has been proposed in the Automatic Control Laboratory of EPFL. This method is based on loop-shaping in the Nyquist diagram under infinity norm constraints on closed-loop MIMO transfer functions (Galdos et al. 2010). Various types of robust MIMO controllers for the case of varying input material compositions and large time delays have been designed using convex optimization techniques (de Oliviera 2011; Oliveira, Amrhein et al. 2011; Dreyfus 2017).

The aim of this project is to apply this method to the design of blending controllers for plants with large, possibly varying time delays as well as low-frequency averaged measurements. A novel multi-rate robust controller and a robust Smith-Predictor controller will be compared in terms of stability and performance for the two MIMO applications. Simulations will be done off-line in Matlab/Simulink using a simplified model and in real-time using a link to Siemens Simatic WinAC using a nonlinear model implemented in O_Blend.

MER: Alireza Karimi

Type of project: Semester or Master

Contact: Dr. Michael Amrhein

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

Gaussian Processes – a machine learning method, is gaining attention for identifying nonlinear dynamical systems due to numerous advantages. However, the Gaussian processes are a computationally expensive tool which limits their application for real-time control and/or embedded control systems. 

In this work, we will deploy Gaussian processes on FPGAs using the LAfF – a code generation toolbox developed at Automatic Control Laboratory. We will consider model predictive control coupled with Gaussian processes for fault tolerant control with case studies of various flight control scenarios.

Prerequisite: C programming skills are required. Familiarity with FPGAs, Gaussian Processes, and Model Predictive control is desired but not required.

Keywords: Machine learning, Field Programmable Gate Arrays, Model Predictive Control, Gaussian Processes, Embedded systems

Professor: Colin Jones

Type of project: Semester or Master

Contact: Harsh Shukla

Nonlinear Model Predictive Control (NMPC) is an advanced control method that utilizes a dynamic model of a system to solve a finite time Optimal Control Problem (OCP) in an iterative fashion. The direct or mathematical programming methods seek to transform a continuous time OCP into a finite dimensional nonlinear program by using some parametrization of the state and control functions.

The pseudospectral collocation is a numerical technique that employs a polynomial approximation of the state and control trajectories to discretize continuous OCPs. The method is widely used to solve trajectory optimization problems that arise in aerospace applications since it provides high numerical accuracy for long prediction horizons. Recent advances in the computational performance of available hardware make it possible to apply pseudospectral collocation to generate real-time model predictive feedback control laws for complex and highly nonlinear systems such as unmanned aerial vehicles, mobile robots, self-driving cars, and industrial manipulators.

  • Efficient collocation methods for Differential Algebraic Equations (DAE): Optimal control of systems governed by DAEs is of great practical importance in robotics (multi-link manipulators, inverted pendulums, wind energy kites and so on). Solving DAEs, however, is not an easy task, since it often requires reformulation of system equations and stabilisation of numeric solutions. In this project, the student will get familiar with the recent advances in numerical methods for DAE. Then an efficient implementation of a chosen collocation method will be tested on a (possibly multi-link) inverted pendulum.
  • First-order solvers for fast NMPC: The goal of this project is to study the performance of first-order methods for Nonlinear Programs resulting from the spectral discretisation of OCPs. The student will study and analyse the state-of-the-art gradient-based optimisation algorithms. A test optimisation problem will be provided for benchmarking.
  • Optimal Control of systems with uncertainties: Stochastic Differential Equations arise in practical applications when only probabilistic knowledge is available for some of the model’s parameters (model has uncertain parameters). Standard methods of solving these equations (e.g. Monte-Carlo simulation) are associated with a considerable computational burden. The Polynomial Chaos Expansion methods allow for efficient propagation of the stochastic state statistics (e.g. mean and variance) through nonlinear dynamics, and therefore, have potential for application in fast SMPC. In this project, the student will implement the Stochastic Galerkin or the Stochastic Collocation algorithm to safely control a mobile robot with uncertain parameters using MPC.

Requirements: Strong mathematical background, familiarity with system and control theory, experience with C++. Knowledge of Eigen/Boost/CasADi libraries is a big advantage.

Professor : Colin Jones

Type of project: Semester / Master

Contact:   Petr Listov 


Industrial projects (current)

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. 

Goal 

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.

Responsibility 

* 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

Requirements 

* 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

Literature 

* 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

A swiss company active in the field of 3D printing and converging digital manufacturing dedicates its efforts to offer high-end 3D printing instruments and solutions.

Extrusion is a typical process used in 3D printing applications. By this process the material is pushed with a controlled force through a nozzle in order to form a fibre with constant section. Many suitable materials are however characterized by a highly non-Newtonian behaviour. Therefore, the process contains non-linearity that can be compensated or linearized near interested regions. Based on measurements and theoretical concepts, the goal of this project is to develop a method to identify a dynamic model of the dispensing process. Different approaches combining classical methods of linear system theory can be applied.

The proposed project gives the opportunity to realize concrete applications in the very interesting and breakthrough domain of additive manufacturing.

MER: Alireza Karimi

Type of project: Master in industry

Contact: Alireza Karimi

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

Elevators are today guided by steel rails which must be placed in a very precise way in order to guarantee a smooth elevator ride. The installation of these rails is very labor intensive and requires highly skilled workforce. In this master thesis, the vision of a guide rail less elevator shall be investigated.

In a first phase, a concept for a guide rail less elevator shall be worked out. Several actuators including additional motors, counter-masses and dampers shall be placed in the elevator system in order to compensate the horizontal forces which would need to be taken by the guide rails. In a second phase, an associated Simulink model shall be set up in order to verify the concept and to show both limits and chances of the concept of a guide rail less elevator.

This master thesis dos not aim to get an overall concept of a guide rail less elevator considering all possible aspects. We would focus on the question of how modern mechatronic actuator technology and the control thereof can contribute to the vision of a guide rail less elevator.

The master thesis is conducted together with the New Technologies Department of Schindler Elevators Ltd.

MER: Alireza Karimi

Type of project: Master in EPFL plus internship 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.

A Reaction Sphere (RS) has been proposed as a potential alternative to traditional Attitude and Orbit Control System (AOCS) actuators based on reaction wheels. The RS consists of an 8-pole permanent magnet spherical rotor that is magnetically levitated and can be accelerated about any axis by a 20-pole stator with electromagnets. A schematic of the RS actuator is depicted in Fig. 1. A RS laboratory prototype has been manufactured to validate force and torque models. More recently, a spherical rotor optimized to improve its manufacturability has been developed.

Classical dynamic controllers have been developed to levitate the rotor inside the stator (magnetic bear- ing) and to control the rotor angular velocity. Preliminary closed-loop measurements using the developed prototype have been performed showing the ability of simultaneously levitating the rotor while rotating it about a given arbitrary axis. During rotation, the performance of the bearing deteriorates due to rotor-orientation- dependent force and torque errors, rotor non-sphericity and mechanical/magnetic unbalance, test bench alignment problems, and deformations of the stator due to rotor weight. To address these issues, a novel pro- totype of reaction sphere with reduced distortion of the rotor magnetic flux density and improved mechanical properties has recently been developed.

The proposed project aims at investigating new techniques for magnetic bearing and angular velocity control. Furthermore, notch-filtering techniques could be studied as a countermeasure to minimize vibrations during rotation. The proposed activities include a literature review of existing techniques, the development of control algorithms, the implementation and evaluation of the developed algorithms in MATLAB/Simulink, and the implementation of the algorithms on the dSPACE real-time control platform for experimental validation using the RS prototype.

MER: Alireza Karimi

Type of project: Internship with Master in industry

Contact: Alireza Karimi

This project is carried out in collaboration with CSEM in Neuchatel.

The eBeeX drones by Sensefly must follow a predefined path (flight plan) and take picture at given positions with external disturbances such as wind. The goal of this project is to improve the quality of the eBeeX flight controller. The project includes: acquisition of flight data and system identification, translation of high level specifications into control specifications, control law synthesis and eventually embedded implementation.

Domaine(s) d’activité: Automatique et mécatronique

Responsable : Karimi Alireza

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 efficient configuration and 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 three key aspects:

  • The automated design of filters in order to accommodate different sensor availability configurations.
  • 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 establishment of a simulation environment that allows to testbench the controller in different scenarios

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, and have the opportunity to use real data from test sites and face actual practical challenges of controller deployment.

Requirements:

  • Control and estimation/filtering knowledge, knowledge of MPC preferred, knowledge of Kalman filtering, numerical optimization, and/or machine learning a plus.
  • 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 energy systems and energy management is 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