Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Karachalios, Dimitrios"'
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 (LPV) formula
Externí odkaz:
http://arxiv.org/abs/2405.09209
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 obstacles. W
Externí odkaz:
http://arxiv.org/abs/2405.02030
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 proposed strateg
Externí odkaz:
http://arxiv.org/abs/2404.14845
In this study, we are concerned with nonlinear model predictive control (NMPC) schemes that, through the linear parameter-varying (LPV) formulation, nonlinear systems can be embedded and with a sequential quadratic program (SQP) can provide efficient
Externí odkaz:
http://arxiv.org/abs/2403.19195
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 and highly
Externí odkaz:
http://arxiv.org/abs/2402.12092
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 the explic
Externí odkaz:
http://arxiv.org/abs/2310.01049
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 dynamic mod
Externí odkaz:
http://arxiv.org/abs/2307.06031
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 corresponding
Externí odkaz:
http://arxiv.org/abs/2208.10124
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 original model i
Externí odkaz:
http://arxiv.org/abs/2112.01258
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 function. The
Externí odkaz:
http://arxiv.org/abs/2012.02075