Profile photo of P. Sopasakis

About Me

I was born in Athens, Greece, in 1985. I received a Diploma in Chemical Engineering in 2007 and an MSc with honours in Applied Mathematics in 2009 from NTU Athens. In December 2012, I defended my PhD thesis titled "Modelling and Control of Biological and Physiological Systems" at NTU Athens. In January 2013 I joined the Dynamical Systems, Control and Optimization research unit at IMT Lucca as a post-doctoral Fellow. Afterwards, I worked as a post-doctoral researcher at ESAT, KU Leuven. I am currently a post-doctoral researcher at KIOS Center of Excellence, University of Cyprus. My research focuses on model predictive control and numerical optimization.

Contact Details

Pantelis Sopasakis
KIOS Research Center
University of Cyprus
1 Panepistimiou Avenue
2109 Aglantzia, Nicosia, Cyprus
Email: contact info (safe image)

Publications

Optimisation

Optimisation Research

MPC THEORY

MPC research

BioMedical

Medical research

Applications

Application-oriented research

KIOS Summer School 2018

In September 2018, I gave a lecture entitled Identification of nonlinear systems by kernel methods (part 1, part 2) at KIOS Graduate Summer School 2018 on Intelligent Systems and Control.

The lecture was a 90' introduction to system identification with special focus on kernel-based methods and was followed by a competition on kaggle where the participants used input-output data to identify a discrete-time nonlinear system.


P&O EAGLE, KU Leuven

I taught the control theory and autonomous navigation module (lectures and lab exercises) of the course P&O Eagle at ESAT, KU Leuven. P&O Eagle is a drone with computer vision and navigation capabilities.

  1. Course slides
  2. Quaternion-based modelling
  3. Addendum: quaternions
  4. C programming guide
  5. MATLAB programming guide
  6. Video lectures

Here is a video presentation made by students...


Control Systems, IMT Lucca

In the winter semester of 2016 I gave the PhD course advanced topics in control systems in IMT Lucca. The course focused on economic model predictive control, control of linear and nonlinear Markovian switching systems, risk measures and risk-averse optimal control.

  1. Economic MPC
  2. Markov jump linear systems
  3. Optimal control for MJLS
  4. Control of Markovian switching systems
  5. Stochastic optimisation and MPC
  6. Risk measures
  7. Risk-averse optimisation
  8. Exercises

Highlights

SuperSCS: Fast and accurate conic solver

SuperSCS is a fast solver for large-scale conic problems of the form

\begin{eqnarray*} && \mathrm{Minimize}\ c' x \\ &&Ax + s = b\\ &&s\in\mathcal{K}, \end{eqnarray*}

where \(A\in\mathbb{R}^{m\times n}\) is a (sparse) matrix, and \(\mathcal{K}\) is a closed, convex, proper cone. SuperSCS is based on the algorithmic scheme SuperMann applied to a Douglas-Rachford splitting on the self-dual homogeneous embedding of the original problem.

SuperSCS achieves higher accuracy and faster convergence compared to SCS as you can see for example here.

Risk-averse Model Predictive Control

Risk-averse model predictive control (MPC) offers a control framework that allows one to account for ambiguity in the knowledge of the underlying probability distribution and unifies stochastic and worst-case MPC.

Risk-averse MPC can be employed in presence of ambiguity on the knowledge of the actual probability distribution of the system disturbances.

We have studied risk-averse MPC problems for constrained nonlinear Markovian switching systems using generic cost functions, and derive Lyapunov-type risk-averse stability conditions by leveraging the properties of risk-averse dynamic programming operators.

We propose a controller design procedure to design risk-averse stabilizing terminal conditions for constrained nonlinear Markovian switching systems.

Additionally, we cast the resulting risk-averse optimal control problem in a favourable form which can be solved efficiently and thus deems risk-averse MPC suitable for embedded applications.

NEW: Checkout our new MATLAB toolbox that allows the formulation and solution of general risk-averse problems with risk constraints.

RapidNet

RapidNet is an open-source software written in CUDA-C++ involving open-source libraries (cuBLAS and rapidjson) which allows the easy design of stochastic model predictive controllers for drinking water networks taking into account the volatility associated with water demands and price uncertainty in a deregulated bid-based energy market.

Some of the features of RapidNet are:

  1. Efficient operating management of drinking water networks
  2. Uncertainty on demands and electricity prices
  3. Mitigation of the volatility in electricity cost
  4. Very fast Stochastic Model Predictive Control
  5. Parallelization on NVIDIA Graphics Processing Units (GPU)

Embedded Nonlinear MPC

This is an ANSI C embedded control framework for nonlinear model predictive control which implements a very fast nonconvex optimization solver with convergence guarantees that significantly outperforms SQP/SCP approaches. It is particularly suitable for robotic applications.

In this video, the embedded nonlinear predictive controller is used to drive an autonomous ground vehicle...

and on a micro aerial vehicle...


Fast Online Compressive Sensing

Compressed sensing is a signal processing methodology for the reconstruction of sparsely sampled signals and it offers a new paradigm for sampling signals based on their innovation, that is, the minimum number of coefficients sufficient to accurately represent it in an appropriately selected basis. Compressed sensing leads to a lower sampling rate compared to theories using some fixed basis and has many applications in image processing, medical imaging and MRI, photography, holography, facial recognition, radio astronomy, radar technology and more.

The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed (Freris et al., 2013): the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements.

In this work we developed a recursive compressed sensing algorithm which makes use of a Forward-Backward Newton (FBN) method to efficient solve the involved LASSO problems in real time. Our simulations showed significant speed-up compared to all well-established algorithms for such problems such as (F)ISTA, ADMM and the L1LS algorithm by Kim et al. Exploiting the fact that in recursive LASSO the optimizer at time k can be used to produce a good estimate at time k+1, and using the fact the FBN converges to the solution locally quadratically, at every sampling time we only need to perform a few Newton iterations.

Jaqpot Quattro

JAQPOT Quattro is a comprehensive open-source web application for linking molecular structure with biological properties and effects of chemical compounds and nanomaterials to living organisms. JAQPOT Quattro uses machine learning algorithms to extract knowledge out of structured databased.

Stochastic control of large-scale water networks

Drinking water networks are large-scale systems whose operation is characterised by complex uncertainty patterns. Pressures across the network need to be kept in certain limits, tanks must not overflow and water demand requirements must be met, while unexpectedly high demands and electricity prices may signal the alarm in the control centre of the network. Water demand and energy price are fluctuating uncertain parameters that deem the control of these systems rather challenging and dictate computationally intense operations for the determination of how valves and pumps need to be operated for a smooth, economic and safe operation of the whole infrastructure.

We have proposed as optimisation-based control scheme which accounts for the aforementioned uncertainty. The resulting optimisation problems involve more than 1 million decision variables and has to be solved in real time. Modern high-end CPUs require up to several minutes for their solution; We employed an accelerated proximal gradient (APG) algorithm which can be vastly parallelised and solved on a GPU to afford computation times down to a few tens of a second! These developments pave the way for the use of optimisation-based control algorithms (such as stochastic model predictive control) in large-scale water networks which - as simulations show - can lead to a significant reduction of energy consumption, higher quality of service and a smoother operation of the network.

NEWS

Special issue on Visual Perception for Micro Aerial Robots

In am a guest editor for the special issue on Visual Perception for Micro Aerial Robots in Journal of Intelligent and Robotic Systems, Springer.

Together with Prof. George Nikolakopoulos, LTU and Prof. Andreas Nuechter, Uni. Würzburg, we seek to attract high quality manuscripts on a series of topics related to vision and guidance, navigation and control (GNC) for unmanned aerial vehicles (UAVs) and especially aerial robots.

The purpose of this special issue is to address theoretical and application-oriented problems in the general area of visual perception for micro-aerial robots and to identify and provide key perception solutions that meet the real-time constraints posed by aerial vehicles, following recent advances in computer vision and robotics.

Topics of interest include (but are not limited to):

  1. Vision-based control and visual servoing
  2. Visual navigation, mapping, and SLAM
  3. Cooperative perception using multiple platforms
  4. Vision-assisted floating-base manipulation
  5. Deep Learning for visual perception
  6. Object recognition, tracking, semantic and 3D vision techniques
  7. Fusion of vision with other sensing systems, e.g., laser scanner
  8. Advanced visual sensors and mechanisms (event-based, solid state sensors, LiDAR, RGB-D, time-of-flight cameras, etc.)
  9. Aerial robot applications on key enabling perception technologies
  10. Model predictive control for vision-based autonomous navigation
  11. Reinforcement learning for visual perception.

Manuscripts should describe original and previously unpublished results which are not currently considered for publication in any other journal.

All manuscripts shall be submitted electronically, and will undergo a peer-review process.

For further details, please, consult the Journal website or contact me or the Guest Editors.

Check out the announcement on WikiCFP.

  • No man ever steps in the same river twice, for it's not the same river and he's not the same man.

    Heraclitus, 535-475 BCE
  • Mathematics knows no races or geographical boundaries; for mathematics, the cultural world is one country.

    D. Hilbert, 1862-1943
  • One can imagine that the ultimate mathematician is one who can see analogies between analogies.

    S. Banach, 1892-1945
  • The realization that life is absurd cannot be an end, but only a beginning. This is a truth nearly all great minds have taken as their starting point. It is not this discovery that is interesting, but the consequences and rules of action drawn from it.

    A. Camus, 1913-1960
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