activity
20212024
collaborators

6 papers

math.OC2024

Closed-Loop Identification and Tracking Control of a Ballbot

Tobias Fischer, Dimitrios S. Karachalios, Ievgen Zhavzharov +1

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…

math.OC2024

Parameter Refinement of a Ballbot and Predictive Control for Reference Tracking with Linear Parameter-Varying Embedding

Dimitrios S. Karachalios, Hossam S. Abbas

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…

math.OC2023

Error Bounds in Nonlinear Model Predictive Control with Linear Differential Inclusions of Parametric-Varying Embeddings

Dimitrios S. Karachalios, Maryam Nezami, Georg Schildbach +1

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 b…

eess.SY2023

On the Design of Nonlinear MPC and LPVMPC for Obstacle Avoidance in Autonomous Driving

Maryam Nezami, Dimitrios S. Karachalios, Georg Schildbach +1

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 mode…

math.DS2022

Bilinear realization from input-output data with neural networks

Dimitrios S. Karachalios, Ion Victor Gosea, Kirandeep Kour +1

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 f…

eess.SY2021

A framework for fitting quadratic-bilinear systems with applications to models of electrical circuits

Dimitrios S. Karachalios, Ion Victor Gosea, Athanasios C. Antoulas

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…