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Relying on Dalian University of technology, the Key Laboratory of intelligent control and optimization of industrial equipment was approved by the Ministry of education on January 15, 2019.

Relying on the control science and engineering and related information technology disciplines of Dalian University of technology, the laboratory actively gives play to the advantages of interdisciplinary platform. Based on the northeast, facing the whole country. Taking innovation as the driving force, we should improve the theoretical and technical research level, knowledge innovation ability and high-level personnel training ability of advanced and intelligent control of industrial equipment in China. Strive to build an important applied research base for training talents, scientific research and achievement transformation, which is leading in the field of information at home and well-known in the world.

1、Main research direction

Research direction 1: advanced control and optimization of complex system

(1) Hybrid system and time-delay system control;

(2) Group intelligent system decentralized control;

(3) Networked control;

(4) Intelligent optimization of complex industrial system.

Research direction 2: intelligent control technology of industrial equipment

(1) Intelligent detection and control technology of industrial equipment;

(2) Industrial control embedded software and system;

(3) Integrated control technology of aeroengine.

Research direction 3: industrial system optimization and decision making

(1) Industrial Internet information processing and decision-making;

(2) Perception, modeling and information fusion of industrial process and scene;

(3) Prediction and optimization technology of industrial energy system

2、Main research contents

Based on the above research directions, the main research contents are as follows.

(1) Prediction and optimization of industrial energy system

For the energy system of industrial production and manufacturing enterprises or expansion to industrial parks, it aims at various types of energy media, such as electric power, heat, gas, new energy, etc. At present, there are some unsolved problems, such as relatively independent management and control, low comprehensive energy efficiency, high cost and great impact on the environment. It adopts deep integration of Internet of things, data-driven, artificial intelligence and other technologies. In response to the challenges of core scientific issues such as multiple time / space scales of multiple energy media, high uncertainty of supply and demand of source and load, and difficulty in collaborative optimization of multiple energy flows in industrial parks, a unified modeling method for multiple energy flows is studied. A new method of industrial energy system prediction and Optimization Oriented to "energy production" two-way interaction, "energy information" security, mechanism data collaborative drive, solves the theoretical and technical problems of large-scale industrial enterprises or industrial parks multi energy flow intelligent control.

(2) Integrated control technology and application of Aeroengine

Aeroengine is a very complex aerodynamic thermodynamic system, which has the characteristics of multi-mode, time-delay, uncertainty, discontinuous dynamics and so on. In the background of aeroengine application, the research on robust control and optimization of complex switched systems with time-delay, unknown uncertainty, discontinuous nonlinear dynamics and asynchronous switching is planned. On this basis, the research of aeroengine control and Optimization Based on complex switching system control theory is carried out. At the same time, the high performance aeroengine provided by Shenyang Engine Research Institute of China Aviation Development Corporation is taken as the object to verify the above research.

(3) Theory and technology of industrial Internet system

Aiming at the core challenges such as complex structure, multi network integration and multi production link description of the existing industrial Internet system of intelligent factory, a series of basic cutting-edge issues of "theory and technology of industrial Internet system of intelligent factory" were studied. This paper focuses on the heterogeneous network integration strategy and the dynamic allocation method of network resources in the complex industrial Internet system composed of Internet, intelligent factory control network and fieldbus. The hybrid system modeling method and quality index evaluation system for industrial Internet system, as well as the transmission performance optimization technology of industrial Internet system, are used to improve the stability of industrial Internet system of intelligent factory.

(4)Intelligent industrial manufacturing process

Carry out basic theoretical innovation and research and development of key technologies for industrial manufacturing process intelligence. This direction aims at the urgent demand of large-scale process industry and discrete manufacturing industry for intelligent product development, manufacturing process, service and other links. The key technologies of manufacturing process situation awareness, system modeling and scheduling decision-making based on multi heterogeneous industrial big data sets, such as design data, manufacturing data and operation data, are studied. The data analysis and decision-making application system software of large-scale manufacturing enterprise driven by mechanism data knowledge model is developed. And in the domestic typical large-scale industrial enterprises to achieve demonstration application and promotion.

(5) Environment perception, modeling and control of intelligent robot

It focuses on the needs of human-machine cooperation, cooperative decision-making and long-term reliable operation of intelligent robots in industrial environment. Especially for the challenges of the complex three-dimensional environment perception, modeling, autonomous behavior planning and human-computer cooperation of mobile robots under the strong dynamic interference and unstructured environment. The research focuses on the theories and methods of human-computer natural interaction and cooperation decision-making, dynamic map construction and maintenance, task scheduling and path planning, autonomous work scenario understanding, etc. based on multi-source perceptual data fusion in large-scale and strong interference environment. In order to improve the autonomous environment perception and adaptability of mobile robots for large-scale industrial equipment manufacturing and assembly to diverse production scenarios. In addition, the efficiency and quality of work should be improved through the cooperation between robot and human, which is an important part of the research on multimodal fusion analysis and interactive intention understanding of human-computer cooperation behavior, as well as the compliance control and trajectory planning of cooperative robot based on reinforcement learning, so as to ensure the efficient realization of human-computer cooperation in industrial environment.