Dimitrios S. Karachalios
YOU?
Author Swipe
View article: Stochastic Error Bounds in Nonlinear Model Predictive Control with Gaussian Processes via Parameter-Varying Embeddings
Stochastic Error Bounds in Nonlinear Model Predictive Control with Gaussian Processes via Parameter-Varying Embeddings Open
This study utilized the Gaussian Processes (GPs) regression framework to establish stochastic error bounds between the actual and predicted state evolution of nonlinear systems. These systems are embedded in the linear parameter-varying (L…
View article: Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region
Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region Open
Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving. This paper proposes control designs for solving reference tracking problems in autonomous driving tasks while considering static o…
View article: Closed-Loop Identification and Tracking Control of a Ballbot
Closed-Loop Identification and Tracking Control of a Ballbot Open
Identifying and controlling an unstable, underactuated robot to enable reference tracking is a challenging control problem. In this paper, a ballbot (robot balancing on a ball) is used as an experimental setup to demonstrate and test propo…
View article: Efficient Nonlinear MPC by Leveraging LPV Embedding and Sequential Quadratic Programming
Efficient Nonlinear MPC by Leveraging LPV Embedding and Sequential Quadratic Programming Open
In this paper, we present efficient solutions for the nonlinear program (NLP) associated with nonlinear model predictive control (NMPC) by leveraging the linear parameter-varying (LPV) embedding of nonlinear models and sequential quadratic…
View article: Parameter Refinement of a Ballbot and Predictive Control for Reference Tracking with Linear Parameter-Varying Embedding
Parameter Refinement of a Ballbot and Predictive Control for Reference Tracking with Linear Parameter-Varying Embedding Open
In this study, we implement a control method for stabilizing a ballbot that simultaneously follows a reference. A ballbot is a robot balancing on a spherical wheel where the single point of contact with the ground makes it omnidirectional …
View article: Error Bounds in Nonlinear Model Predictive Control with Linear Differential Inclusions of Parametric-Varying Embeddings
Error Bounds in Nonlinear Model Predictive Control with Linear Differential Inclusions of Parametric-Varying Embeddings Open
In this work, we provide deterministic error bounds for the actual state evolution of nonlinear systems embedded with the linear parametric variable (LPV) formulation and steered by model predictive control (MPC). The main novelty concerns…
View article: On the Design of Nonlinear MPC and LPVMPC for Obstacle Avoidance in Autonomous Driving
On the Design of Nonlinear MPC and LPVMPC for Obstacle Avoidance in Autonomous Driving Open
In this study, we are concerned with autonomous driving missions when a static obstacle blocks a given reference trajectory. To provide a realistic control design, we employ a model predictive control (MPC) utilizing nonlinear state-space …
View article: A data-driven nonlinear frequency response approach based on the Loewner framework: preliminary analysis
A data-driven nonlinear frequency response approach based on the Loewner framework: preliminary analysis Open
We propose a hybrid method based on the combination of computed-aided nonlinear frequency response analysis with the Loewner framework, for the characterization of nonlinear dynamical processes with application in electrochemistry. The met…
View article: Irreversible electroporation for stage III locally advanced pancreatic cancer: Single-center experience
Irreversible electroporation for stage III locally advanced pancreatic cancer: Single-center experience Open
Introduction: Irreversible Electroporation (IRE) is a non-thermal ablation technique with promising results for treating locally advanced pancreatic cancer (LAPC). This study was conducted to evaluate safety and efficacy of IRE in the mana…
View article: Bilinear realization from input-output data with neural networks
Bilinear realization from input-output data with neural networks Open
We present a method that connects a well-established nonlinear (bilinear) identification method from time-domain data with neural network (NNs) advantages. The main challenge for fitting bilinear systems is the accurate recovery of the cor…
View article: A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits
A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits Open
We propose a method for fitting quadratic-bilinear models from data. Although the dynamics characterizing the original model consist of general analytic nonlinearities, we rely on lifting techniques for equivalently embedding the original …
View article: A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits
A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits Open
In this contribution, we propose a data-driven procedure to fit quadratic-bilinear surrogate models from data. Although the dynamics characterizing the original model are strongly nonlinear, we rely on lifting techniques to embed the origi…
View article: On computing reduced‐order bilinear models from time‐domain data
On computing reduced‐order bilinear models from time‐domain data Open
We propose a procedure for identification of reduced‐order bilinear models from time‐domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data corresponding to the re…
View article: Data‐driven (Bilinear) identification and reduction
Data‐driven (Bilinear) identification and reduction Open
Identifying dynamical systems from measured data is an important step towards accurate modeling and control. Model order reduction (MOR) constitutes a class of methods that can be used to replace large, complex models of dynamical processe…
View article: The Loewner framework for nonlinear identification and reduction of Hammerstein cascaded dynamical systems
The Loewner framework for nonlinear identification and reduction of Hammerstein cascaded dynamical systems Open
We present an algorithm for data‐driven identification and reduction of nonlinear cascaded systems with Hammerstein structure. The proposed algorithm relies on the Loewner framework (LF) which constitutes a non‐intrusive algorithm for iden…
View article: The Clinical Outcome in Patients with Peritoneal Metastasis
The Clinical Outcome in Patients with Peritoneal Metastasis Open
Cytroreductive surgery (CRS) and HIPEC are controversial effective treatment options for selected patients with peritoneal metastases. We retrospectively examined 4.500 patients with peritoneal metastases from different tumors from 2005 to…
View article: Learning reduced-order models of quadratic control systems from input-output data
Learning reduced-order models of quadratic control systems from input-output data Open
In this paper, we address an extension of the Loewner framework for learning quadratic control systems from input-output data. The proposed method first constructs a reduced-order linear model from measurements of the classical transfer fu…
View article: Assessment of the Efficacy of Microinvasive Glaucoma Surgery Techniques using an Oculopression Stress Test
Assessment of the Efficacy of Microinvasive Glaucoma Surgery Techniques using an Oculopression Stress Test Open
Background Glaucoma is one of the most common causes of blindness worldwide. The only evidence-based treatment to slow down the progression of glaucoma is the reduction of intraocular pressure (IOP) using local medication or through surger…
View article: On Bilinear Time Domain Identification.
On Bilinear Time Domain Identification. Open
The Loewner framework (LF) in combination with Volterra series (VS) offers a non-intrusive approximation method that is capable to identify bilinear models from time-domain measurements.
View article: Learning reduced-order models of quadratic control systems from input-output data
Learning reduced-order models of quadratic control systems from input-output data Open
In this paper, we address an extension of the Loewner framework for learning quadratic control systems from input-output data. The proposed method first constructs a reduced-order linear model from measurements of the classical transfer fu…
View article: A bilinear identification‐modeling framework from time domain data
A bilinear identification‐modeling framework from time domain data Open
An ever‐increasing need for improving the accuracy includes more involved and detailed features, thus inevitably leading to larger‐scale dynamical systems [1]. To overcome this problem, efficient finite methods heavily rely on model reduct…
View article: Case study: Approximations of the Bessel Function
Case study: Approximations of the Bessel Function Open
The purpose of this note is to compare various approximation methods as applied to the inverse of the Bessel function of the first kind, in a given domain of the complex plane.