Gábor Rödönyi

Dynamics of ocular surface topography in healthy subjects

Our topography system is an enhancement of a standard TMS‐1 corneal topograph instrument (Computed Anatomy Inc., New York, NY, USA). Topographic images are captured at a rate of 4 s−1, allowing the recording of a series of 120 images in 30 s after a complete blink. In this prospective preliminary study 15 healthy volunteers were examined. The main outcome measures were the time profile of changes in surface regularity index (SRI), surface asymmetry index (SAI) and simulated keratometry values (K1, K2). After a blink there was a tendency for improvement in ocular surface regularity. Later trends were less clear. Our topography system makes possible the detailed evaluation of tear‐film dynamics in the post‐blink period. The new technique may play an important role in the diagnosis of various tear‐film abnormalities; the results may also have significant implications in the planning of refractive surgeries.

Analysis and experimental verification of faulty network modes in an autonomous vehicle string

Advanced autonomous vehicle strings rely on inter-vehicle communication in order to decrease the necessary safety gap so that fuel consumption can be decreased and road capacity can be increased. In case of failures of some communication channels, the corresponding back-up control strategy must be switched on. Maximal spacing errors of such back-up modes are analyzed and compared. Robustness to platoon heterogeneity and communication delays are considered. The main conclusion we can draw is that, in the full communication mode, satisfactory spacing performance can be achieved by using simple output-feedback controllers designed without detailed knowledge on engine/brake characteristics, only utilizing the existing services available today in every commercial heavy trucks with automatic gearbox. Experimental verification of the designed controllers are presented.

Application of LPV/LFT Modeling and Data-based Validation to a re-entry vehicle

*† ‡ § ** †† ‡‡ In this article an application of LFT/LPV modeling and data-based validation techniques to a re-entry vehicle is shown. This work is part of a European Space Agency project tasked with examining the use of LPV technologies for the control design process of space systems. The application presented serves as an assessment on the technological readiness level of LPV/LFT modeling approaches and data-based validation algorithms, as well as a review of their features, shortcomings and needs. The selected vehicle is the longitudinal nonlinear motion of NASA HL-20 during an approach trajectory from Mach 4.5 down to 1.5.

Guaranteed peaks of spacing errors in an experimental vehicle string

Abstract Controllers for autonomous vehicle strings usually consist of local feedback controllers responsible for performing acceleration demands and a common control law aimed at the string stability of the entire platoon. Based on analysis of robust peak-to-peak performance and experimental verification, it is shown in the paper that satisfactory spacing performance can be achieved without accurate knowledge on the engine/brake/gear systems, only by using a common control law that relies on inter-vehicle communication and the commercial on-board services of todays heavy vehicles.

Unfalsified uncertainty modeling for computing tight bounds on peak spacing errors in vehicle platoons

A joint uncertainty modeling and robust performance analysis method is proposed for describing brake/driveline actuator uncertainties in heavy vehicles and calculating tight upper bounds on peak spacing errors due to these uncertainties in autonomous vehicle platoons. Evaluation of robust performance of the platoon in terms of peak-to-peak norm is imperative in determining minimal safety gaps. The method can be characterized as a constrained nonlinear optimization problem that can be efficiently solved by pattern search algorithms. Actuator uncertainties are modeled by filtered ℓ∞ disturbances. The set of consistent filters are determined from experimental data based on unfalsification. From this set of filters the one is selected that provides the smallest upper bounds on peak spacing errors. Calculations based on experimental data are presented.

Uncertainty Identification for a Nominal LPV Vehicle Model Based on Experimental Data

In this paper a practical method is presented for modelling uncertainty of a nominal linear parameter-varying (LPV) vehicle model. The aim with the uncertainty model is to bound the nominal model-error and satisfy robust stability and performance objectives during robust control design. Existing frequency-domain model-validation methods are applied to perform the first aim. The linear fractional uncertainty structure and the distribution of nominal model-error among the uncertainty blocks and disturbances are chosen to perform the second aim. The paper is motivated by the problem of steering a vehicle by alternately braking the front wheels in emergency situations. The identification is performed on real experiment data. The method and the results are demonstrated on a yaw-rate tracking problem and μ-controller design on constant scheduling variable of the LPV model. Using the proposed algorithm, on the supposition that nominal model error remains below the bound estimated from the validation data set, an unfalsified model is constructed for robust control guaranteeing robust performance against worstcase uncertainty and disturbance.

A joint structured complex uncertainty identification and ε-synthesis algorithm

A joint uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations in the general linear fractional transformation (LFT) form and additive disturbances - customary representation in modern robust control and 2) to select from this set according to closed-loop control objectives. The motivation is to avoid conservatism of physics-based uncertainty modelling yet giving confidence in the model. The algorithm works on sampled, bounded-energy experimental data on the frequency-domain and integrates model invalidation/construction and control synthesis in order to achieve robust performance. Standard D-K iteration steps are combined with an optimization step on a group of selected data. The efficiency and applicability of the method is demonstrated on a vehicle control problem with real experimental data

Identification of an LPV vehicle model based on experimental data for brake-steering control

Abstract A physically parameterized continuous-time velocity-scheduled LPV state-space model of a heavy-truck is identified from measurement data. The aim is to develop a model for controller which steers the vehicle by braking either the one or the other front wheel. It can be applied in many vehicles, where the sole possibility to automate the steering in emergency situations, like e.g. unintended lane departure, is the application of the electronic brake system. Such steering controllers usually require the prediction of the yaw rate and the steering angle on every possible velocity. This problem defines the requirements for the model. Four different order model structures are derived from a certain physical description. Assuming state and output noise, all of them are identified in parameter-varying observer form using prediction error method. The quadratic criterion function is composed from measurement data of several different experiments. Each experiments are carried out on constant velocities but the cost is constituted from different velocity experiments. That structure is selected for controller design which has the best cost on test data out of those the poles of which are in the control bandwidth. The poles are defined on constant velocity. The resulted nominal model consists of the feedback connection of the yaw dynamics with one state-variable and the steering system dynamics with two states and of a first order actuator dynamics with time-delay The predicted outputs show a good fit to the measurements.

Analysis of Input Delay Systems using Integral Quadratic Constraint

The L2-gain computation of a linear time-invariant system with state and input delay is discussed. The input and the state delay are handled separately by using dissipation inequality involving a Lyapunov-Krasovskii functional and integral quadratic constraints. A conic combination of IQCs is proposed for characterizing the input delay, where the coefficients are linear time-invariant systems. The numerical example (a vehicle platoon) confirm that using this dissipativity approach a more effective method for L2-gain computation is obtained.

Robust LPV controller synthesis with uncertainty modeling

The paper presents an iterative uncertainty modeling and robust controller synthesis method for linear parameter-varying (LPV) systems in linear fractional transformation (LFT) form. The goal of the method is to improve robust performance by exploiting modeling freedom in the structured uncertainty descriptions while ensuring model consistency with respect to measurement data. The proposed iterative algorithm consists of three steps: robust LPV controller synthesis, tuning of the uncertainty model and performing closed-loop experiments for new validation data.