Механизмы интеллектуальных обратных связей, обработки знаний и самообучения в системах управления автономными роботами и мультиагентными робототехническими группировками

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

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.

In the control of autonomous mobile robots there exist two types of control: global control and local control. The requirement to solve global and local tasks arises respectively. This chapter concentrates on local tasks and shows that robots can learn to cope with some local tasks within minutes. The main idea of the chapter is to show that, while creating intelligent control systems for autonomous mobile robots, the beginning is most important as we have to transfer as much as possible human knowledge and human expert-operator skills into the intelligent control system. Successful transfer ensures fast and good results. One of the most advanced techniques in robotics is an autonomous mobile robot on-line learning from the experts’ demonstrations. Further, the latter technique is briefly described in this chapter. As an example of local task the wall following is taken. The main goal of our experiment is to teach the autonomous mobile robot within 10 minutes to follow the wall of the maze as fast and as precisely as it is possible. This task also can be transformed to the obstacle circuit on the left or on the right. The main part of the suggested control system is a small Feed-Forward Artificial Neural Network. In some particular cases – critical situations – “If-Then” rules undertake the control, but our goal is to minimize possibility that these rules would start controlling the robot. The aim of the experiment is to implement the proposed technique on the real robot. This technique enables to reach desirable capabilities in control much faster than they would be reached using Evolutionary or Genetic Algorithms, or trying to create the control systems by hand using “If-Then” rules or Fuzzy Logic. In order to evaluate the quality of the intelligent control system to control an autonomous mobile robot we calculate objective function values and the percentage of the robot work loops when “If-Then” rules control the robot.

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 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.

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

Рассмотрена проблема совершенствования роторных окорочных станков, которые во всех технологиях лесопереработки лесопромышленных стран используются в обязательном порядке. Несмотря на достаточно отработанную конструкцию, тем не менее, основные технологические операции станка не оснащены современными адаптивными системами автоматического управления (САУ). Ранее были предложены разработки на основе пневмогидропривода с использованием автоматического управления на основе нечеткой логики. В предложенной системе автоматического управления выполняется стабилизация заданного усилия прижима режущего инструмента – короснимателя. Однако заданное усилие зависит от ряда технологических параметров, которые характеризуются неопределенностью, и проблема управления заданным прижимом инструмента осталась нерешенной. Таким образом определилась цель исследований, которая заключалась в создании интеллектуальной системы автоматического управления заданным прижимом короснимателя окорочного станка. Решались следующие задачи: 1) разработка схемы интеллектуального управления короснимателем; 2) разработка схемы обобщенной интеллектуальной системы управления в виде нейронечеткой сети; 3) постановка задачи управления заданным прижимом инструмента; 4) обоснование входных и выходных переменных задачи (фаззификация); 5) разработка базы правил нечеткой системы; 6) выполнение нечетких выводов для промежуточных и заключительного слоев сети в среде Matlab; 7) реализация модели интеллектуальной системы в среде Matlab+Simulink. Результатами работы является модель интеллектуальной системы управления короснимателем и ее программная реализация в среде Simulink для использования в практике проектирования роторных окорочных станков. The problem of improving the rotary debarkers, which are used without fail in all timber processing technologies of the timber industry countries, is considered. Despite the sufficiently developed design, nevertheless, the main technological operations of the machine are not equipped with modern adaptive automatic control systems (ACS). Previously, developments based on a pneumatic hydraulic drive were proposed using automatic control based on fuzzy logic. In the proposed automatic control system, the stabilization of a given pressing force of the cutting tool – the debarker is performed. However, the given force depends on a number of technological parameters, which are characterized by uncertainty, and the problem of controlling the given clamping of the tool remains unsolved. Thus, the goal of the research was determined, which was to create an intelligent system for automatic control of a given pressure of the debarker staple lifter. The following tasks were solved: 1) development of an intelligent control scheme for the debarker; 2) development of a diagram of a generalized intelligent control system in the form of a neuro-fuzzy network; 3) setting the task of controlling the given clamping of the tool; 4) justification of the input and output variables of the problem (fuzzification); 5) development of a fuzzy system rule base; 6) execution of fuzzy conclusions for intermediate and final layers of the network in the Matlab environment; 7) implementation of the model of an intelligent system in the Matlab + Simulink environment. The results of the work are a model of an intelligent control system for the debarker and its software implementation in the Simulink environment for use in the practice of designing rotary debarkers.

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.

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.

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.

A supplement to Fu [1] is presented. Three areas are briefly reviewed: 1) control systems with human controller, 2) control systems with man-machine controller, and 3) autonomous robot systems. Problems for further research are discussed.