Арктическая ветродизельная электростанция с интеллектуальной системой автоматического управления

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.

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.

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.

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.

In order to help the Vertical Roller Mill (VRM) working stably with higher automation level and lighter labor intensity, an intelligent automatic control system is designed by analyzing the characteristics and technology of the VRM grinding process, where both start/stop operation and normal work can be executed in automatic mode. Besides, Interlock protection is introduced so as to improve the security of the proposed system, which has not only improved the automation level of VRM system, but also guaranteed the stability and reliability effectively.

Lighting system control techniques in commercial buildings: Current trends and future directions

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.

Revolutionizing gear hobbing machine precision: Innovative thermal error compensation and intelligent error control with enhanced crystal structure algorithm and self-attention bidirectional minimal gated unit network

Objective To investigate the clinical effectiveness of an intelligent shell temperature control system in deep tumor hyperthermia for controlling the temperature in the outer shell of chest and abdominal malignancies. Methods From 2015 to 2016, a randomized comparative study was carried out in the intelligent temperature control system (experimental group containing 37 patients with chest tumor and 26 patients with abdominal tumor) and conventional cooling by replacement of water bags (control group containing 37 patients with chest tumor and 25 patients with abdominal tumor) for chest and abdominal tumor hyperthermia. The effectiveness in controlling the outer shell temperature was compared between the two methods. Results In the experimental group, the first boot of the cooling system occurred at 24.1 and 21.8 minutes on average after the hyperthermia started and the average number of system boots was 2.5. In the control group, patients reminded to replace the water bag at 33.1 and 33.8 minutes on average after the hyperthermia started and the average number of replacements was 3.6. The highest outer shell temperature in patients with chest tumor and abdominal tumor averaged 38.5 and 39.1 ℃ at the first boot of the cooling system in the experimental group and 39.3 and 40.4 ℃ at the first replacement of the cooling water bag in the control group, respectively. The treatment with and without the intelligent temperature control system caused grade ≥Ⅱ empyrosis in 1 and 4 patients with chest tumor and in 0 and 3 patients with abdominal tumor, respectively. Conclusions In deep tumor hyperthermia, compared with the conventional cooling by replacement of water bags, the intelligent shell temperature control system can detect and cool the high-temperature zone in the outer shell at an earlier time, which effectively reduces the incidence of skin burn injury. Key words: Deep hyperthermia; Scalded skin; Temperature control

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.

Relevance. The growing need for energy-efficient lighting solutions and the development of smart home technologies create the need to develop intelligent lighting control systems. Modern systems must adapt to changing environmental conditions, ensure user comfort and significantly reduce energy consumption. Research object: intelligent lighting control systems based on microcontrollers with functions of adaptation to environmental conditions. Purpose of the article: conducting an analytical review of existing intelligent lighting control solutions, determining optimal technical solutions and identifying promising development directions. Research results: A comprehensive analysis of modern intelligent lighting control systems was conducted, key technologies of sensors, microcontrollers and adaptation algorithms were considered. The advantages and disadvantages of the main commercial solutions were identified, and optimal hardware and software platforms were identified. Conclusions. Intelligent lighting control systems demonstrate significant potential for energy savings (up to 60%) and increased user comfort. The most promising solutions are based on 32-bit ARM Cortex-M microcontrollers with integration of multiple sensors and machine learning algorithms.

Intelligent systems often violate fundamental usability principles, such as control and transparency. Explanations have been shown to have a positive effect on transparency in such systems but little research exists as to how it may affect control. We set out to investigate how explanations may impact upon users' perceptions of control in an intelligent system. We conducted an empirical study in which 15 participants carried out a qualitative data analysis task using an intelligent system. Participants were divided into two groups: with and without explanations. Participants could indicate agreement or correct the system. Our results show that participants without explanations display more control-exerting behaviors but that there is no difference between conditions in participants' perception of control. We discuss our findings and implications for future work.