Development Of Intelligent Control System Research Articles

Динамическая модель отопительных приборов и систем

In order to develop previously performed work we consider the problem of constructing a dynamic mathematical model of heating devices and systems. The heating system at the same time seems to be equivalent heater. The output value of the model in this case is the power (heat capacity). Decision is executed by the "gray box", that is the structure of the model is determined by analyzing the physics of the processes occurring in heating devices and systems, and the numerical values of its parameters are found from the experimental data by parametric identification. The resulting differential equation is linear with respect to the unknown quantity-power heater(heating), input the same size-water inlet temperature of the heating system and the temperature of the indoor air are included in this equation in a linear fashion, and the flow is in non-linear manner. It is shown that a constant flow of coolant model can be represented by the transfer functions in the iso-mapping Laplace transfer function of the channel "coolant temperature at the inlet to the heater(heating system) – thermal capacity of the heater(heating)" is a inertia component of the first order, and the channel "indoor air temperature-heat capacity of the heater(heating)" - inertia-forcing component with a negative coefficient of transmission. The qualitative features of the model parameters, in particular, their dependence on the coolant flow are analyzed. Algorithmic block diagram of the control object, which includes the heating system and the thermal regime of the building itself is provided. The results of the parametric model identification from experimental data is described. The model can be used in the development of intelligent control systems heating buildings.

METHODOLOGY FOR DETERMINING THE VALUE OF COMPLEXITY PARAMETER FOR EMERGENCY SITUATION DURING DRIVING OF THE TRAIN

Purpose. During development of intelligent control systems for locomotive there is a need in the evaluation of the current train situation in the terms of traffic safety. In order to estimate the probability of the development of various emergency situations in to the traffic accidents, it is necessary to determine their complexity. The purpose of this paper is to develop the methodology for determining the complexity of emergency situations during the locomotive operation. Methodology. To achieve this purpose the statistical material of traffic safety violations was accumulated. The causes of violations are divided into groups: technical factors, human factors and external influences. Using the theory of hybrid networks it was obtained a model that gives the output complexity parameter of the emergency situation. Network type: multilayer perceptron with hybrid neurons of the first layer and the sigmoid activation function. The methods of the probability theory were used for the analysis of the results. Findings. The approach to the formalization of manufacturing situations that can only be described linguistically was developed, that allowed to use them as input data to the model for emergency situation. It was established and proved that the exponent of complexity for emergency situation during driving the train is a random quantity and obeys to the normal distribution law. It was obtained the graph of the cumulative distribution function, which identified the areas for safe operation and an increased risk of accident. Originality. It was proposed theoretical basis for determining the complexity of emergency situations in the train work and received the maximum complexity value of emergency situations that can be admitted in the operating conditions. Practical value. Constant monitoring of this value allows not only respond to the threat of danger, but also getting it in numerical form and use it as one of the input parameters for the locomotive intelligent control system. The decision on further control actions will be based on it.

Design of the Temperature Control System for Tobacco Leaf Redrying

The development of intelligent control system based on environmental parameters tobacco redrying. The use of the FNN neural network to determine the temperature and humidity of the air conditioning units running time, on this basis, the paper presents an improved network of four framework structure. The model has a simple calculation; each layer of the network the derivation are obvious physical meaning, stability control safety and energy conservation in the actual operation.

Development of Intelligent Control Systems for Bioreactors

Recent development of intelligent bioreactor systems has made significant evolution in terms of tools, objectives, and concepts. Stimulated by rapid progress in the field of AI backed up with powerful personal computers, AI-bascd computer control of bioreactors is now under remarkable progress.The present paper review the recent development of intelligent control systems for bioreactors. Paticular attention is focused on fuzzy expert systems, neuro control, neuro-fuzzy control, and genetic algorithm.

Simultaneous Parameter Identification and States Estimation using Neural Networks

A major step toward the development of intelligent control systems (ICS) is mathematical formulation of techniques for learning and identification of dynamical systems. Such techniques may be used for establishing true adaptive control systems, failure diagnostics, pattem recognition, and model- based fuzzy control systems. This paper presents a general technique of parameter identification and adaptive state estimation for linear time varying and time invariant systems. The proposed technique is based on the Hopfield network and Luenberger observer design. Mathematical derivations for systems represented in a state space form and those defined based on input/output maps are described. Simulation results for different cases are presented.

Bridging the gap between conventional and intelligent control

A control engineering perspective on modeling and representation issues, nonlinear analysis, design implementation, and evaluation of intelligent control systems is developed. The importance of using a control engineering approach to assess the contribution of the field of intelligent control is discussed. The ways in which technology has provided a driving force for the field of intelligent control are explained, and several research directions for control engineers working in the area of intelligent control are presented. The ways in which the control engineer's use of modeling, analysis, design, simulation, implementation, and experimental evaluation provide a sound engineering approach to the development of intelligent control systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Immunoglobulin G subclasses concentrations in patients with Kawasaki disease

Recent development of intelligent bioreactor systems has made significant evolution in terms of tools, objectives, and concepts. Stimulated by rapid progress in the field of AI backed up with powerful personal computers, AI-bascd computer control of bioreactors is now under remarkable progress.The present paper review the recent development of intelligent control systems for bioreactors. Paticular attention is focused on fuzzy expert systems, neuro control, neuro-fuzzy control, and genetic algorithm.

Studies on the influence of hemostatic molecular markers and platelet derived growth factor on endothelial damage in Kawasaki disease

Recent development of intelligent bioreactor systems has made significant evolution in terms of tools, objectives, and concepts. Stimulated by rapid progress in the field of AI backed up with powerful personal computers, AI-bascd computer control of bioreactors is now under remarkable progress.The present paper review the recent development of intelligent control systems for bioreactors. Paticular attention is focused on fuzzy expert systems, neuro control, neuro-fuzzy control, and genetic algorithm.

Structural Safety Control and Stochastic Aspects

Recently, increasing interest has been shown in structural safety design us ing active and hybrid control systems. This interest is driven by the rapid development of intelligent control systems, advanced sensor technology, signal processing, and successful applications in the control of large flexible space structures.