Интеллектуальное управление инструментом окорочного станка

Обоснован перечень задач самообучения интеллектуальных систем управления автономными роботами и мультиагентными робототехническими группировками. Обсуждаются основные проблемы создания интеллектуальных самообучающихся систем управления. Показано, что один из наиболее перспективных подходов к реализации процессов самообучения в интеллектуальных системах управления автономными роботами и многоагентными робототехническими группировками связан с использованием метода автоматического построения деревьев классификации. Приводятся результаты комплексного моделирования.

Ensemble of long short-term memory prediction and variable universe fuzzy control for seismic response reduction using electromagnetic dynamic vibration absorber

A description of a new class of organizational and technical systems is given – intelligent road-building compaction control systems, the principles of operation of which provide for ensuring a given quality of compaction of asphalt mixtures in the process of road construction. The development of systems of that class is due, in the first place, to the transition to digital and intelligent control systems in construction. A scientific problem related to the relevance of creating methods for intelligent control of compaction of asphalt mixtures that affect the quality of road surfaces was formulated. To succeed, the usage of neural network structures was proposed. The usage will allow real-time monitoring of changes in the compaction quality index and efficient controlling of road machines using both the driver's manual means and the automatic compaction control system. The properties of the system under study – which determine the principles of its functioning: intelligence, informativity, speed, interconnectedness of subsystems, controllability and modularity – were presented. The above principles of the functioning of the proposed system, which are part of the methodology for managing the quality of asphalt compaction of pavement roads, were implemented in a set of methods and algorithms. Among them should be noted a new method of intelligent continuous control of compaction in the process of laying asphalt mixtures, an advanced method of intelligent prediction of compaction quality by vibrating rollers.

With the progress of computer technology and intelligent control system, it is necessary to introduce the PLC together with the current systems. In this paper, we conduct research on the new intelligent industrial control and communication system based on PLC technique. As Industry 4.0 at the core of intelligent factory, the products made in its basic idea is integrated with dynamic digital memory, perception and communication ability, carrying the whole supply chain and all kinds of information required for life cycle. PLC biggest characteristic is to constitute the traditional relay model computer software technology, formed a unique style in relay ladder graph based image programming language, the PLC control system in the form of program to achieve its control function. In the simulation section, we implement our proposed system in detail and conduct numerical analtsis and simulation. The result is acceptable and accurate.

Most of the currently existing microclimate control systems operate on the basis of traditional P, PI, PID controllers. But the work of such systems is effective only in a narrow operating range. With significant fluctuations in environment, it is required to reconfigure the parameters of these regulators each time. In such cases, maintaining comfortable conditions in the room is recommended by using intelligent control technologies, so-called fuzzy control systems, operating in terms of fuzzy logic. The aim of the paper is to research the possibility of implementation of intelligent control systems to maintain the comfortable microclimate in the premises and checking the adequacy of the system through simulation. The climate control system is based on fuzzy inference and provides forming the base of fuzzy production rules, fuzzification of input values, aggregation of truth of sub conditions of each rule, activation of conclusions and defuzzification process that generates an output signal to control the smart home functional devices. Procedures of the fuzzy inference and simulation are implemented in the Matlab programming environment using the Fuzzy Logic Toolbox and Simulink packages. The results of the paper showed that the developed intelligent microclimate control system for premises based on fuzzy logic provides a flexible self-tuning of the engineering equipment and maintaining the microclimate parameters at the required level.

With the development of network communication, computer technology and artificial intelligence, it has become an inevitable development trend to promote "intelligent buildings" characterized by intelligence, comfort and safety. In order to solve the shortcomings of the existing research on intelligent building control system, this paper discusses the functional equation of CAN algorithm, artificial intelligence fuzzy controller and intelligent building, and aims at the intelligent building control system based on artificial intelligence and CAN algorithm. Hardware settings and parameter settings are briefly introduced. And the work flow design of the intelligent building control system structure based on artificial intelligence and CAN algorithm is discussed, and finally the application of the intelligent building control system based on artificial intelligence and CAN algorithm is experimentally tested. The correct number of switch control by artificial intelligence and CAN algorithm in the intelligent building control system is relatively high. The correct number of switch control in the interference environment is less than that in the humid environment, and the number of switch operations for the control system is the least in the humid environment. The error rate of 500 and up to 2500 is less than 5%, thus verifying the superiority of the application of intelligent building control system based on artificial intelligence and CAN algorithm.

This paper aims to present an implementation of advanced lighting control system in airport terminal. The lighting requirements of the terminal are fulfilled and regulated by an intelligent control system deployed with i-bus. The intelligent control system adjusts the lighting in terminal at different time according to the real-time requirements. Compared with conventional control system, this intelligent control system can control the lighting in time as long as the real-time requirements are shifted in a remote way. Besides, this new system can also save energy to a great extent.

The main problem faced in supplying power to remote consumers is that there are high logistical costs of delivering fuel and equipment for diesel power plants, poor density of transport infrastructure and, as a consequence, a high cost of fuel. There are also high operating costs at diesel power plants and specific fuel consumption; in addition, there is no monitoring and control automation. In view of a high wind potential of the Arctic territories, energy complexes and systems can be effectively modernized and constructed on the basis of modular wind-diesel power plants with an intelligent control system. A concept of and hardware solutions for an intelligent automatic control system are proposed, the use of which makes it possible to maximize the amount of electricity generated by renewable sources owing to dynamically redistributing the power between the hybrid energy complex components and, as a result, to minimize fuel consumption. An analysis of a controlled wind-diesel power plant has shown that by using an intelligent automatic control system, forecasting the electricity generation by a wind power plant, and arranging storage battery operation in a cyclic mode, it becomes possible to effectively cover the load schedule of an autonomous consumer and increase the extent of diesel fuel substitution by up to 60 % or more.

Based on heat balance analysis, the dynamic heating model of asphalt tank of mixture equipment was established, the heating time and using time nodes were determined, and the trapezoidal dynamic heating process of asphalt tank was also formed to realize the intelligent control and reduce energy consumption. After analyzing different schemes, the control scheme of the combination of feed-forward and feedback to realize intelligent control system for asphalt tank was adopted, which could ensure that the asphalt in the tank is in the operating temperature and the mixing equipment works continuously when the asphalt was used completely. In addition, a simulated analysis of calculation model, which was adopted by intelligent control system, was made in combination of entitative engineering. Results showed that compared with traditional process of heating asphalt, this intelligent heating control system could reduce the heat consumption by 15.8% under the condition of continuous construction.

Bare-bones languages serve small systems with ease: Cushmann, R HEDN Vol 25 No 16 (5 September 1980) pp 171–177

The advanced technology of the Internet and extensive agricultural transformation and upgrading have made the overall agricultural industry chain and modern agriculture better. In view of the high cost and suffering of traditional agricultural planting management, the Internet of Things (I O T) is applied to agriculture to realize real-time detection and intelligent management of crop growth conditions, remote control, and change the traditional agricultural equipment planting mode. The purpose of this article is to design and research the I O T automatic control (A C) system for smart agriculture. This article first introduces the core technology of the I O T through an overview of the basic theories of the I O T. Combined with the current status of agricultural automation in my country, the existing problems and deficiencies are analyzed. On this basis, use the core technology of the I O T to supplement and improve it. This article systematically expounds the overall scheme design, module function design and A C algorithm realization of the I O T brake control system. And use field investigation method, comparative analysis method and other research forms to carry out research on the theme of this article. Experimental research shows that the sampling data of the greenhouse is selected as the sample, the appropriate initial membership function is selected, and the fuzzy control rules obtained through the training of the fuzzy neural network algorithm are relatively correct. The output result of the automatic temperature control system is mostly consistent with the actual data on site. Overall, the temperature A C system designed in this subject can meet the A C requirements of agriculture.

This report presents a generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of fuzzy controllers by supplying a self-organizing capability. It is demonstrated that fuzzy controllers prepared to maintain control object in the prescribed conditions are often fail to control when such a conditions are dramatically changed. We propose the solution of such kind of problems by introducing a generalization of strategies in fuzzy inference from a set of pre-defined fuzzy controllers by new quantum fuzzy inference based systems (prototype of intelligent system of systems engineering). We stress our attention on the robustness features of intelligent control systems.

An adaptive controller is proposed using a novel interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC). The T2FCMAC realizes an interval type-2 fuzzy logic system based on the structure of the cerebellar model articulation controller (CMAC). Due to the better ability of handling uncertainties provided by type-2 fuzzy sets, it can solve some complicated problems with outstanding effectiveness than type-1 fuzzy sets. In addition, an intelligent control system is proposed; this control system is comprised of a T2FCMAC and an auxiliary compensation controller. The T2FCMAC is utilized to approximate a perfect controller and the parameters of T2FCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and the T2FCMAC. Finally, a Chua's chaotic circuit is performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance.

Based on the requirements of the offshore wind farm construction, an intelligent remote control system has been proposed for jacking-up offshore platform. The system consists of a wireless LAN, a master control system, an intelligent node control system, and front sensors. The master control system is composed of a master control decision-making expert system, a malfunction diagnosis and disposal decision-making expert system, and database system, a virtual reality display system, and a wireless LAN system. The intelligent node control system consists of a control decision-making expert system, a jacking control system, a locking control system, a malfunction diagnosis system, executing controllers, status sensors, a database system, a pier-side control interface, and wireless LAN system. This remote control system enables a real-time analysis of the status of the platform, self adjusting of the platform, and memorizing previous decision for a better efficient control. This system supports independent execution with fault diagnosis & recovery ability. So it is recommended because it meets the requirements of offshore platform of wind farm construction.

With the worsening of the global environment and the strengthening of people's awareness of environmental protection, the air pollution caused by the exhaust emissions of marine diesel engines has attracted extensive attention from the international community. Because the exhaust post-treatment technology does not need to be reformed for diesel engine, and the quality requirement of fuel is relatively low, domestic and foreign research and development institutions all attach great importance to the research of exhaust post-treatment technology. By analyzing the mechanism and main chemical reaction of SCR reaction, the overall design scheme of marine diesel engine exhaust intelligent emission reduction control system was completed in order to reduce the nitrogen oxides and solid particles in the exhaust of marine diesel engine. According to TierIII emission regulation and marine diesel engine exhaust, the hardware structure design and model selection of marine diesel engine exhaust intelligent emission control system are completed. In order to ensure that the emission of the control system can meet the requirements of nitrogen oxide conversion rate and ammonia gas escape quantity at the same time, the corresponding model prediction algorithm is designed in this paper. Through the analysis of experimental results, it is proved that the control system established in this paper can well meet the emission requirements of TierIII regulations. The research content of this paper can be applied in the field of shipping to meet the requirements of TierIII regulations and prepare for more stringent emission regulations.