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 have held post-doctoral positions at IMT Lucca, Italy, ESAT, KU Leuven, Belgium and KIOS CoE, University of Cyprus. I will soon join the School Of Electronics, Electrical Engineering & Computer Science at Queen's University Belfast as a lecturer.
Pantelis Sopasakis
Queen's University Belfast
School of EEECS
i-AMS Centre
Email: to be updated
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.
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.
Here is a video presentation made by students...
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.
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 (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 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:
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...
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 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.
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.
We proudly present SuperSCS: a super-(linearly) fast solver for conic optimization problems. Optimization software often cast convex problems into conic ones and delegate their solution to solvers in the backend. Based on the implementation of the popular solver SCS, we implemented a new algorithm which leads to manifold speed-ups.
Software, OptimizationThis is a CUDA-implementation of an dual accelerated proximal gradient algorithm for scenario-based model predictive control problems. The structure of the optimization problem is employed to accelerate computations; very high speedups (ranging from x10 up to x100) are observed compared to the corresponding GPU implementation.
GPU, Numerical OptimizationA remarkably fast algorithm for recursive compressive sensing which is about one order of magnitutde faster compared to state-of-the-art algorithms (L1LS, ISTA, FISTA).
Numerical OptimizationDrinking 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. Scenario-based stochastic model predictive control is an advanced control methodology which can address the problem of controlling such systems very efficiently leading to a considerable reduction in operating costs and a higher quality of service.
Stochastic MPC, Control, DWNJAQPOT Quattro is a predictive toxicology platform on the web which combines machine learning, cheminformatics, linked data and web technologies with the OpenTox and eNanomapper APIs to deliver a generic and interoperable solution for drug discovery.
Software, ChemiformaticsThis 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.
Software, Optimization, ControlIn 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):
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.