Ivan Nagy

Adaptive cross-direction control of paper basis weight

Abstract The initial setting and maintaining of the desired paper basis weight (in machine direction and cross direction) is to be ensured adaptively for several given paper grades. Technical equipment includes: usual traversing gauge at the output of the paper machine, special traversing robot for shaping the headbox lip (sequential setting of screws), control computer. Adaptive multivariate LQG control with recursive identification generalized to distributed parameter system has been designed with the following features: integral (convolution type) model of the process respecting the continuous nature of all signals and kernels involved (by means of spline approximation), repetitive control synthesis (via Riccati equation with periodical solutions).

TRAFFIC MODEL OF A MICROREGION

Abstract This paper introduces a new concept of the state model of one traffic microregion based on a maximum utilization of information from all measured traffic variables. The aim of the model is to estimate length of queues that are formed on arms of junctions with traffic lights. This task is trivia in case of complete knowledge of all measured traffic quantities for all junction arms. Then the model only counts simply the queue length from input and output intensities. However, the net of all needed detectors is not usually complete and some significant traffic flows (parking cars, etc.) are not measurable in practice. The model estimates the queue length in this case. In the end of the paper, the model and estimation algorithm is tested for several types of disturbances which can arise in reality. At least partially, these experiments illustrate the functionality and effectiveness of the proposed model for estimating queue lengths on the junction arms in the real traffic.

Polynomial LQ Control Synthesis for Delta-Operator Models

Abstract The present-day discrete LQ control synthesis is mostly based on ARUA-type models. The synthesis can be well algorithmized but it does not yield satisfactory results when sampling period is small with regard to dominant time-constants of the plant. In this case, the equations get ill-conditioned and the synthesis meets difficulties. Limiting of sampling period to zero cannot be made within the theory. In the paper an attempt has been made to overcome these problems by using difference-type discrete models. Such approach also tries to contribute to the trend to reduce a gap between the discrete control and the continuous one which is still felt in engineering practice. The algorithms of both discrete and continuous control synthesis are presented in a unified way and properties of the results are illustrated by means of a simple example.

Initial Description of Multi-Modal Dynamic Models

Multiple models, neural networks, cluster analysis and probabilistic mixtures are prominent examples of situations when complex multi-modal models [1] are built using vast amount of data. Complexity and non-unicity of modified situation imply that resulting description depends heavily on the initial phase of search. The safest repetitive purely random search is mostly inhibited by computational complexity of the addressed task. For this reasons, various techniques have been designed. None of them, to our best knowledge, suits to cases when dynamic models are constructed. The paper describes a novel technique that fills this gap in a promising way. Essentially, the trial description is gradually split whenever there is possibility that a unimodal sub-model hides more modes.

Fuel Consumption Optimization: Early Experiments*

Abstract The paper deals with a problem of fuel consumption optimization. Solutions existing in this field are mainly based on the various conceptual approaches such as hybrid and electric vehicles. However, it leads to high initial cost of a vehicle. The approach presented in this paper aims at conventional vehicles and is based on recursive algorithms of system identification and adaptive quadratic optimal control under Bayesian methodology. Experiments with real data measured on a driven vehicle are provided.

Expert-based initialization of recursive mixture estimation

Initialization is an extremely important part of the mixture estimation process. There exists a series of initialization approaches in the literature concerning the mixture initialization. However, the majority of them is directed at initialization of the expectation-maximization algorithm widely used in this area. This paper focuses on the initialization of the mixture estimation with normal components based on the recursive statistics update of involved distributions, where the mentioned methods are not suitable. Its key part is the choice of the initial statistics. The paper describes several relatively simple initialization techniques primarily based on processing the prior data. The experimental part of the paper represents results of validation on real data.

Recursive estimation of mixtures of exponential and normal distributions

The paper deals with estimation of a mixture of normal and exponential distributions with the dynamic model of their switching. A separate estimation of normal or exponential mixtures is solved by various approaches in many papers over the world. However, in some application areas, data are of such a nature that they should be described by a combination of exponential and normal models. The paper proposes a recursive Bayesian algorithm of estimation of such a mixture based on continuously measured data. Specific tasks the paper solves are: (i) parameter estimation of both the types of components; (ii) parameter estimation of the dynamic switching model and (iii) detection of the currently active component. Results of experiments with real data are demonstrated.

SERVO PROBLEM WITHIN FUEL CONSUMPTION OPTIMIZATION

The presented paper deals with a problem of fuel consumption optimization. Today’s automotive industry solves this problem mainly via various conceptual approaches (hybrid and electric vehicles). However, it leads to high initial cost of a vehicle. This paper focuses on fuel economy for conventional vehicles. For this aim, recursive algorithms of adaptive optimal quadratic control under Bayesian methodology are used. A stochastic servo problem, including setpoint tracking, is a part of the considered adaptive control design. In this paper, fuel consumption and speed of a driven vehicle are the controlled variables, where the first one is to be optimized and the second one is pushed to track its set-point. This set-point is a recommended roaddependent speed. Experiments with real data measured on a driven vehicle are provided.

Mathematical application for departure model

In the light of todays high traffic volume, it is crucial to anticipate the traffic flow on traffic lights controlled intersections. Without this knowledge, it would be impossible to control the traffic in complicated hubs, such as, for instance, those in Prague. One of the characteristics, through which we can describe dynamics of vehicle movement on traffic lights controlled intersections, is the interval between departure of a vehicle from the space in front of the stop line and the arrival to the actual stop line. This is called a departure model. Our aim is, first, to create an appropriate mathematical application, which would take into account individual variables influencing departure of individual vehicles and determine the dependence among these variables. Second to create such a departure model, which would, on one hand, correspond as much as possible to the contemporary traffic situations, but on the other hand, be more exact for specific intersections.

Factorized EM Algorithm for Mixture Estimation

A classical version of the EM algorithm is considered in the paper. Its numerical properties are improved using factorized algorithms for maximization in M step of the algorithm. The results are illustrated on simulated examples.