Predict Lab Student Projects

Motivation:

The Automatic Control Lab develops Model Predictive Control schemes for a miniature race car system. Innovative methods will have a wide scope of applications in driving safety and/or autonomous driving.

Modeling the tire properties (longitudinal and side slip forces) turns out to be challenging for a 1/27-scale. This is due to difficulties collecting informative and consistent experimental data but also uncertainty about the physical effects that should be covered by the model structure.

Description:

The goal of this project is to continuously learn and improve the racing control performance of the mini race car while it is driving. Intuitively, we would like to imitate the situation where a driver gets to conduct a new unknown car on a new unknown race track. The driver will carefully explore the safe driving envelope of the car on the track while improving the lap time.

We want to explore different methods at the intersection between system identification, controller tuning, and machine learning. The exact approach will be discussed with the student depending on the state of the project and the student’s skills.

Skills needed

– Mechanical understanding, ideally knowledge about car dynamics

– Proficiency in C++ (for controlling the car and sensor interface)

– Proficiency in Matlab (for data analysis)

– Familiarity with Python/PyTorch (for Machine Learning)

– Significant experience in coding projects

– Familiarity with ROS/ROS2 (Robot Operating System) is a plus

– Familiarity with Eigen, Casadi is a plus (for modeling/parameter optimization)

– Optimal Control (e.g., MPC) is a plus

Comment

Please contact Johannes Waibel ([email protected]) if interested.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Johannes Christian Karl Waibel

Administration

Nicole Anne Bouendin

Commercial buildings consume around 40% of the world’s energy. Many research papers have explored different advanced methods to save energy. For example, model predictive control (MPC) does the job and guarantees occupancy comfort. However, there is still a large gap between the promising research results and real-world deployment. Thus, this project explores a more implement-friendly building control framework – data-driven control methods.

Current industrial practice is typically simple controllers such as bang-bang and PI controllers. To update these simple controllers to more advanced optimization/ learning-based control, such as predictive model control and reinforcement learning, additional investment in computation infrastructure is needed. One low-cost alternative solution is directly tuning those parameters of existing simple controllers to optimize the building performance. For example, Bayesian optimization has been demonstrated to be effective in tuning building controllers. This project could explore more experimental work on Bayesian optimization-based building controller tuning. A building testbed in the EPFL campus, Polydome (shown in the image below), is ready for actual experiments.

Requirements:

1. Proficient in Matlab or Python

2. Knowledge in optimal control or machine learning is preferred

3. Knowledge in Bayesian optimization or derivative-free optimization is a plus

Comment

Assistants: Jicheng Shi ([email protected]), Wenjie Xu ([email protected])

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Christophe Salzmann

Administration

Nicole Anne Bouendin

Motivation:

In the previous semesters, we build a lightweight hovercraft platform capable of hovering on an air-hockey table. The long-term goal of the project would be the autonomous play against humans, or having multiple hovercrafts playing air hockey autonomously against each other.

Description:

In this project, we want to develop and deploy sensor fusion (Kalman filter) and control algorithms onboard the microprocessor of the hovercraft (ESP32). For this, you will build upon our existing micro-ROS implementation running on FreeRTOS. We have data from the onboard sensors (accelerometer, velocity sensor, and gyroscope) which have to be fused with delayed external position measurements (Optitrack) to obtain a high-frequency state estimate. Using this state estimate, the aim is to develop an onboard low-level controller tracking trajectories given from an offboard computer.

Skills needed:

– Excellent knowledge of C/C++

– Knowledge about Control Systems is a plus

– Familiarity with ROS/ROS2 is a plus

If interested, please contact the responsible assistant directly ([email protected]) and attach your transcripts and CV.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Roland Schwan

Administration

Nicole Anne Bouendin

Motivation:

In previous semesters, we build a lightweight hovercraft platform capable of hovering on an air-hockey table, developed a software/hardware stack based on ROS2, identified the system dynamics using machine learning techniques, and implemented some rudimentary control algorithms to position the hovercraft on the table freely. This project aims at tieing everything together to achieve optimal play against a human or another robot/hovercraft.

Description:

In this project, we want to achieve full autonomous play. For this, we need to develop a meta-planning algorithm which calculates a strategy to where, when, and how to hit the puck, playing it back optimally hitting the enemy goal. This will involve the interplay between optimal decision-making and optimal control, giving a good mix of mathematical modeling, algorithm development, software engineering work, and making things work in simulation and a real hardware platform. Additionally, there is the possibility to incorporate machine learning tools like Gaussian processes to learn and correct for potential model mismatch in real-time, and/or using Bayesian optimization for iterative performance tuning of hyperparameters.

Skills needed:

– Excellent knowledge of C/C++

– Knowledge in Optimal Control (MPC)

– Autonomy and creativity to come up with solutions on your own

– Familiarity with ROS/ROS2 is a plus

If interested, please contact the responsible assistant directly ([email protected]) and attach your transcripts and CV.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Roland Schwan

Administration

Nicole Anne Bouendin

Motivation:

The EPFL Rocket Team aims for building thrust-vector-controlled rockets that can be reused by controlling them to land upright at a specific location. We develop algorithms that guide and control the rocket during the descent phase, approach, and touchdown.

Description:

In this project, we would like to develop algorithms for safely adapting drone and rocket controllers online, i.e., during the flight.

First, we will establish a baseline method, i.e., an adaptation approach based on conventional model identification/controller design. One approach could be to recursively fit a linear model and update controller gains based on it.

Second, we will develop a Machine Learning method that tunes the existing controller, and/or models a physical submodule, and/or directly approximates a feedforward control law. Eventually, we will compare the performance of the two developed methods.

Skills needed:

– Understanding of flight mechanics, modeling, and identification

– Classical and/or Optimal Control (LQR/MPC)

– Machine Learning methods (Neural Networks/Gaussian Processes)

– Familiarity with Python/PyTorch (for Machine Learning)

– Experience in C++ (for operating the drone)

– Significant experience in coding projects

– Familiarity with ROS (Robot Operating System) is a plus

Comment

Please contact Johannes Waibel ([email protected]) if interested.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Johannes Christian Karl Waibel

Administration

Nicole Anne Bouendin

Motivation:

The Automatic Control Lab develops Model Predictive Control schemes for a miniature race car system. Innovative methods will have a wide scope of applications in driving safety and/or autonomous driving.

Modeling the tire properties (longitudinal and side slip forces) turns out to be challenging for a 1/27-scale. This is due to difficulties collecting informative and consistent experimental data but also uncertainty about the physical effects that should be covered by the model structure.

Description:

The goal of this project is to establish a high-fidelity model of the mini race car by developing new modeling approaches, conducting experiments for data collection, and fitting the models to the data.

One way to improve the data quality will be to integrate an IMU and/or wheel speed sensors into the cars. Given the small size, this requires a careful component selection and mechanical design of attachments/wiring/etc.

A second approach can be to use Machine Learning to describe additional physical effects.

The obtained model will be used in simulation and ideally for model-based control that seizes the newly-gained tire information. Promising controllers will be tested on the real racetrack in return.

Skills needed

– Mechanical understanding, ideally knowledge about car dynamics

– Proficiency in C++ (for controlling the car and sensor interface)

– Proficiency in Matlab (for data analysis)

– Familiarity with Python/PyTorch (for Machine Learning)

– Significant experience in coding projects

– Familiarity with ROS/ROS2 (Robot Operating System) is a plus

– Familiarity with Eigen, Casadi is a plus (for modeling/parameter optimization)

– Optimal Control (e.g., MPC) is a plus

Comment

Please contact Johannes Waibel ([email protected]) if interested.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Johannes Christian Karl Waibel

Administration

Nicole Anne Bouendin

Motivation:

In the previous semesters, we build a lightweight hovercraft platform capable of hovering on an air-hockey table. The long-term goal of the project would be the autonomous play against humans, or having multiple hovercrafts playing air hockey autonomously against each other.

Description:

In this project, we want to steer the hovercraft time-optimally to a given state (position, velocity) to intercept the puck and play it back optimally. For this, we aim to use advanced control algorithms like non-linear time optimal model predictive control to steer the hovercraft at its physical limits. For this, you will develop and deploy high-performance numerical optimization algorithms capable of running in real-time, building upon our existing ROS2 software stack.

Skills needed:

– Excellent knowledge of C/C++

– Knowledge about Optimal Control (MPC)

– Familiarity with ROS/ROS2 is a plus

If interested, please contact the responsible assistant directly ([email protected]) and attach your transcripts and CV.

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Roland Schwan

Administration

Nicole Anne Bouendin

We recently developed and released a high-performance quadratic program (QP) solver based on a proximal interior-point method [1]. While performing well with default settings/hyperparameters, we are interested in finding more optimal and robust default hyperparameters and problem/domain specific hyperparameters to achieve better performance.

Bayesian optimization (BO) is a sample-efficient black-box optimization method, which has been successfully applied to tune the hyperparameters of machine learning models [2]. This project aims to apply the recently proposed BO (e.g., constrained BO [3]) methods to tune the hyperparameters of a QP solver.

Requirements:

– Proficient in Matlab or Python

– Knowledge in optimization or machine learning is preferred

– Knowledge in Bayesian optimization or quadratic programming is a plus

If interested, please contact the responsible assistants directly ([email protected], [email protected]) and attach your transcripts and CV.

References:

[1]: Schwan, R., Jiang, Y., Kuhn, D., & Jones, C.N. (2023). PIQP: A Proximal Interior-Point Quadratic Programming Solver. In IEEE Conference on Decision and Control.

[2]: Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25.

[3]: Xu, W., Jiang, Y., Svetozarevic, B., & Jones, C.N. (2023, July). Constrained efficient global optimization of expensive black-box functions. In International Conference on Machine Learning (pp. 38485-38498). PMLR.

Comment

Assistants: Roland Schwan ([email protected]), Wenjie Xu ([email protected])

Professor(s)

Colin Neil Jones (Laboratoire d’automatique 3), Roland Schwan

Administration

Nicole Anne Bouendin