100 citations · 335 across the 27 of their papers we have counts for
20 papers · 1 filter
Sampled-data optimal control of linear fractional-order systems
Pantelis Sopasakis
This paper studies the problem of optimal control of Caputo linear fractional-order systems of order with continuous-time state constraints and input constraints. The…
GPU-Accelerated SPOCK for Scenario-Based Risk-Averse Optimal Control Problems
Ruairi Moran, Pantelis Sopasakis
This paper presents a GPU-accelerated implementation of the SPOCK algorithm, a proximal method designed for solving scenario-based risk-averse optimal control problems. The propose…
NMPC for Collision Avoidance by Superellipsoid Separation
Ruairi Moran, Sheila Bagley, Seth Kasmann +5
This paper introduces a novel NMPC formulation for real-time obstacle avoidance on heavy equipment by modeling both vehicle and obstacles as convex superellipsoids. The combination…
Parallelisable computation of the gradient in nonlinear stochastic optimal control problems
Jie Lin, Ruairi Moran, Pantelis Sopasakis
Nonlinear (deterministic and stochastic) optimal control problems are often solved on embedded devices using first-order numerical optimisation methods. The gradient computation ac…
SPOCK: A proximal method for multistage risk-averse optimal control problems
Alexander Bodard, Ruairi Moran, Mathijs Schuurmans +2
Risk-averse optimal control problems have gained a lot of attention in the last decade, mostly due to their attractive mathematical properties and practical importance. They can be…
Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems
Ajay K. Sampathirao, Panagiotis Patrinos, Alberto Bemporad +1
Scenario-based stochastic optimal control problems suffer from the curse of dimensionality as they can easily grow to six and seven figure sizes. First-order methods are suitable a…
Optimal operation of microgrids with risk-constrained state of charge
Jie Lei, Christian A. Hans, Pantelis Sopasakis
In this paper we present a stochastic scenario-based model predictive control (MPC) approach for the operation of islanded microgrids with high share of renewable energy sources. W…
OpEn: Code Generation for Embedded Nonconvex Optimization
Pantelis Sopasakis, Emil Fresk, Panagiotis Patrinos
We present Optimization Engine (OpEn): an open-source code generation tool for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines t…
Uncertainty-aware demand management of water distribution networks in deregulated energy markets
Pantelis Sopasakis, Ajay K. Sampathirao, Alberto Bemporad +1
We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity p…
Embedded nonlinear model predictive control for obstacle avoidance using PANOC
Ajay Sathya, Pantelis Sopasakis, Ruben Van Parys +3
We employ the proximal averaged Newton-type method for optimal control (PANOC) to solve obstacle avoidance problems in real time. We introduce a novel modeling framework for obstac…
Risk-averse risk-constrained optimal control
Pantelis Sopasakis, Mathijs Schuurmans, Panagiotis Patrinos
Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate be…
SuperSCS: fast and accurate large-scale conic optimization
Pantelis Sopasakis, Krina Menounou, Panagiotis Patrinos
We present SuperSCS: a fast and accurate method for solving large-scale convex conic problems. SuperSCS combines the SuperMann algorithmic framework with the Douglas-Rachford split…
Aerial navigation in obstructed environments with embedded nonlinear model predictive control
Elias Small, Pantelis Sopasakis, Emil Fresk +2
We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate i…
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
Christian A. Hans, Pantelis Sopasakis, Jörg Raisch +2
In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed…
A Simple and Efficient Algorithm for Nonlinear Model Predictive Control
Lorenzo Stella, Andreas Themelis, Pantelis Sopasakis +1
We present PANOC, a new algorithm for solving optimal control problems arising in nonlinear model predictive control (NMPC). A usual approach to this type of problems is sequential…
Risk-averse model predictive control
Pantelis Sopasakis, Domagoj Herceg, Alberto Bemporad +1
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 unifi…
Stochastic economic model predictive control for Markovian switching systems
Pantelis Sopasakis, Domagoj Herceg, Panagiotis Patrinos +1
The optimization of process economics within the model predictive control (MPC) formulation has given rise to a new control paradigm known as economic MPC (EMPC). Several authors h…
Stabilising Model Predictive Control for Discrete-time Fractional-order Systems
Pantelis Sopasakis, Haralambos Sarimveis
In this paper we propose a model predictive control scheme for constrained fractional-order discrete-time systems. We prove that all constraints are satisfied at all time instants…
Accelerated reconstruction of a compressively sampled data stream
Pantelis Sopasakis, Nikolaos Freris, Panagiotis Patrinos
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 perf…
GPU-accelerated stochastic predictive control of drinking water networks
Ajay K. Sampathirao, Pantelis Sopasakis, Alberto Bemporad +1
Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable…