Control Science and Engineering (081100) - Academic Master's Degree
The Master’s Degree Program in Control Science and Engineering is a comprehensive discipline based on control theory, systems theory, and information theory. It focuses on engineering systems, employing mathematical methods, artificial intelligence, and information technology as primary tools. The discipline studies theories, methods, technologies, and practical applications related to detection, modeling, control, and optimization strategies and systems. Its research contains fundamental theories, engineering design and system implementation, serving as an indispensable theoretical and technical foundation for automation in fields such as machinery, power, chemical engineering, metallurgy, aviation, aerospace and shipbuilding.
The program was granted the authority to confer master's degrees in Industrial Automation in 1993 and became a first level discipline under Control Science and Engineering in 2019. It was designated as a key discipline in Liaoning Province in 2002. It has several provincial and municipal research platforms, including the Liaoning Provincial Key Laboratory of Embedded Technology Applications, the Shenyang Key Laboratory of Artificial Intelligence, the Shenyang Laboratory of Intelligent Perception and Edge Computing, and the Shenyang Key Laboratory of Industrial Intelligent Chips and Network Systems. The discipline aims to enhance the self-perception, self-prediction, self-adaptation, and autonomous optimization capabilities of industrial systems under complex environments and conditions. It addresses challenges arising from nonlinearity, time variability, uncertainty, and unpredictability in practical systems, focusing on data driven modeling, control, optimization, fault tolerant control, intelligent decision making, and coordination control for complex interconnected systems.
In recent years, the discipline has obtained tens of national projects including National Natural Science Foundation projects and national key research and development programs and nearly a hundred provincial and municipal projects. Faculty members have published over 400 papers in high impact journals and conferences. It also has obtained 30 invention patents and published 6 textbooks.
Research Directions:
(1) Control Theory and Control Engineering
This direction focuses on challenges in perception, prediction, control, and optimization for industrial systems in complex environments. It investigates the applications of advanced control theories and methods such as predictive control, adaptive control, switching control, robust control, fault-tolerant control, fuzzy control, and neural network control in complex industrial processes, to provide theoretical foundations and effective solutions to complex industrial process control problems.
(2) Pattern Recognition and Intelligent Systems
This direction focuses on real time detection technologies and automation devices, covering information extraction and processing, system modeling and simulation, application technologies, and system design. Research scope includes intelligent detection methods, pattern recognition, computer vision, multi-agent collaborative control, motion control, unmanned system control, and distributed simulation of complex systems. It primarily focuses on the design of intelligent sensing systems, implementation of optoelectronic sensors, computer vision based detection, precision measurement and control, real time detection techniques, and the research and development of intelligent instrumentation and smart monitoring systems.
(3) Systems Engineering
This direction applies systems science, control science, information science, and applied mathematics to analyze, design, and optimize complex systems in industries such as manufacturing, agriculture, transportation, and defense. Key research areas include enterprise informatization, supply chain and logistics management, production planning and scheduling, smart factories, data analysis, and intelligent decision making, which has established distinctive research thrusts, yielding innovative achievements with significant industrial deployment.
Course Info
Linear System Theory, Pattern Recognition, Intelligent Optimization Methods, Neural Networks and Reinforcement Learning, Digital Image Processing Technology, Machine Vision and Applications, etc.
Program Objectives
Aiming to meet the requirements of national construction and socio-economic development, and addressing the demand for teaching, research, engineering technology development, and management talents in the field of Control Science and Engineering from enterprises, higher education institutions, and research institutes, this program cultivates academically innovative talents with comprehensive development in moral, intellectual, physical, aesthetic, and labor education. Master's degree graduates in this academic discipline should meet the following requirements:
(1) Love the motherland, maintain a firm and correct political direction, possess a high sense of social responsibility, demonstrate sound academic integrity and ethics, and be physically and mentally healthy.
(2) Possess a solid theoretical foundation and systematic specialized knowledge in Control Science and Engineering, and be familiar with the current status and development trends of the research field both domestically and internationally.
(3) Have the ability to conduct scientific research and solve relevant technical problems. Be capable of undertaking theoretical research and systems engineering design in the field of Control Science and Engineering, demonstrating a certain degree of innovation in related works.
(4) Master in at least one foreign language, capable of reading foreign literature in related fields, and possess basic cross cultural communication skills.
Master of Control Engineering (085406) - Professional Degree Program
1. Discipline Overview
Control Engineering is a vital engineering field that applies control theory and technology to meet and fulfill the growing demands for automation and intelligence in modern industries, agriculture, and other socio-economic sectors. It is based on control theory, information theory, and systems theory, with mechanical, electrical, and other engineering systems as its primary research focus. Utilizing modern information technologies such as computer science, network technology, and communication technology, as well as emerging scientific advancements like artificial intelligence, big data, and the Internet of Things, it addresses issues related to the analysis, design, decision-making, and optimization of control systems.
This discipline originated in 1993 as the Industrial Automation major. Its associated control discipline was approved as a ministerial level key discipline by the Ministry of Mechanical Industry in 1995. Following the State Council's discipline adjustment in 1997, it evolved into the current Control Theory and Control Engineering discipline and became a provincial key discipline in 2002. In 2019, it was approved as a first level discipline in Control Science and Engineering, and in 2021, it was authorized by the Ministry of Education to independently establish an interdisciplinary doctoral program in Artificial Intelligence. It addresses major national and regional needs in equipment manufacturing and emerging technologies in Liaoning, focusing on key technical challenges in control engineering. It has distinctive strengths and advantages in areas such as advanced control technologies and systems, industrial systems engineering, intelligent sensing technology and industrial IoT, and intelligent control systems. Over the past five years, it has undertaken more than 50 longitudinal research projects, including national key scientific and technological projects, key initiatives from the Ministry of Industry and Information Technology, major research and development projects from the Liaoning Provincial Department of Science and Technology, and Liaoning Unveiling and Commanding projects.
The discipline boasts several provincial and municipal research platforms, including the Liaoning Provincial Key Laboratory of Embedded Technology Applications, the Shenyang Key Laboratory of Artificial Intelligence, and the Shenyang Key Laboratory of Intelligent Perception and Edge Computing. It has also established joint graduate training bases with institutions such as the Shenyang Institute of Automation (Chinese Academy of Sciences), Northern Heavy Industries Group, and Beijing Neural Network Technology Co., Ltd., providing excellent platforms for cultivating the practical and innovative capabilities of the graduate students.
2. Research Directions
(1) Advanced Control Technologies and Systems
Focusing on complex industrial processes and equipment manufacturing systems as primary research objects, it integrates control theories and methods with intelligent approaches including fuzzy reasoning, neural networks, deep learning, data mining, and reinforcement learning to investigate the theories, methodologies, and technologies for modeling, analyzing, synthesizing, optimizing, designing, and implementing control systems. Specificly, the research areas contain advanced control technologies, mechatronic system control and optimization, networked control systems, distributed control systems, industrial equipment monitoring and fault diagnosis, embedded control systems, enterprise automation and intelligentization, as well as data-driven control.
(2) Industrial Systems Engineering
Focusing on complex industrial systems such as discrete manufacturing, process industries, and power systems, it adopts both manufacturing and systemic perspectives to analyze, model, make decisions, plan, design, manage, operate, evaluate and optimize industrial systems including the structures, elements and processes. The goal is to achieve production optimization objectives including enhanced production efficiency, improved product quality, and reduced production costs and resource consumption. Specific research areas cover modeling and optimization of complex industrial systems, production planning and scheduling, supply chain management and optimization, data analytics and optimization for smart manufacturing systems, smart manufacturing information systems, as well as digital workshop management and optimization.
(3) Intelligent Sensing and Industrial IoT
Centered on Industrial Internet of Things (IIoT), this research direction integrates sensing and monitoring capable sensor units, control units, mobile communication technologies and intelligent analytics into all aspects of industrial production processes, investigating intelligent sensing, detection and control technologies and devices in IIoT environments to achieve intelligent monitoring, control, diagnosis, decision making and maintenance, and then improves production efficiency and reducing energy consumption. Specific research areas encompass intelligent sensing technologies, machine vision inspection, intelligent online detection techniques and methods, IoT communication technologies, condition based and predictive maintenance, smart factory industrial network systems, digital twin technology, real time monitoring of manufacturing processes and equipment, edge computing and network slicing technologies, as well as IIoT-based energy management systems.
(4) Intelligent Control Systems
Addressing dynamic characteristics such as nonlinearity, multivariable coupling, uncertainty, complex mechanistic modeling, and difficulties in establishing precise mathematical models, this research direction employs deep integration of artificial intelligence, computational intelligence, and automatic control to investigate intelligent control of complex systems under uncertain, unpredictable, and dynamic conditions. It focuses on researching, designing, and developing intelligent control systems capable of autonomously recognizing environmental changes and operational status variations, featuring self-adaptation, self-learning, self-adjustment, and self-optimization capabilities to achieve control objectives including safe, reliable, and optimized system operations. Specific applications include intelligent control systems for smart manufacturing and industrial processes, robotic control, smart grid control, modern agricultural control, intelligent transportation and autonomous driving systems, as well as unmanned control systems.
3. Course Info
Control System Analysis and Design, Computer Control Systems, Intelligent Control, Advanced Process Control Technology, Modern Detection Technology, Scheduling Principles and Applications, Data Mining, Pattern Recognition Technology and Implementation, etc.
4. Program Objectives
To meet the needs of national construction and scientific development, and addressing the demand for research, teaching, and technological innovation and management talents in control engineering from enterprises, higher education institutions, and research institutes, this program cultivates well rounded, practice oriented, and innovative talents with strong independent scientific research capabilities in moral, intellectual, physical, aesthetic, and labor education. Master's degree graduates in this discipline should meet the following requirements:
(1) Love the motherland, maintain a firm and correct political direction, possess a high sense of social responsibility, demonstrate sound academic integrity and ethics, and be physically and mentally healthy.
(2) Possess a solid theoretical foundation and expertise in control engineering, master advanced technical methods and modern technological means to solve engineering practice problems in this field, and be familiar with relevant industry standards.
(3) Demonstrate the ability to undertake specialized technical work in a specific area of control engineering, including engineering planning, design, research, development, and management, reflecting a certain degree of practical innovation.
(4) Master in at least one foreign language, capable of reading foreign literature in related fields, and possess basic cross-cultural communication skills.
Professional Master's Degree in Artificial Intelligence (085410)
I. About the Program
Artificial Intelligence is a scientific discipline focused on researching and developing theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. It integrates perception, decision-making, and execution capabilities while encompassing interdisciplinary domains including computer science, mathematics, control theory, information perception and processing, communications, and optimization techniques. China's 14th Five-Year Plan and Long-Range Objectives Through 2035 explicitly mandate fostering and expanding emerging digital industries such as artificial intelligence, charting a strategic roadmap for the field's future development and propelling a new phase of AI technological advancement nationwide.
Launched in 2021, the Master's Program in Artificial Intelligence cultivates research-practice innovation talents engaged in theoretical research, system development/design, and engineering implementation. Graduates excel across domains including computer vision, natural language processing, intelligent system development, and big data. The program leverages provincial/municipal research platforms such as the Liaoning Provincial Key Laboratory of Embedded Technology Applications for Higher Education, Shenyang Key Laboratory of AI, and Shenyang Laboratory for Intelligent Perception and Edge Computing, alongside collaborative training bases with the Shenyang Institute of Automation (Chinese Academy of Sciences), Intelligent Manufacturing Research Institute, and Shenyang Meixing Technology Co., Ltd.
In recent years, faculty in this program have undertaken over 20 vertical research projects—including sub-projects of the National Key R&D Program, NSFC grants, and initiatives funded by Liaoning's Department of Science and Technology and Department of Education—alongside more than 10 industry-sponsored horizontal projects, earning one national/provincial-ministerial level award.
II. Program Focus Areas
A. Machine Learning and Data Modeling
Focusing on complex system modeling in open, stochastically uncertain environments where traditional methods fail to effectively model, analyze, or control subjects, this specialization employs mathematical statistics and optimization techniques integrated with end-to-end machine learning and reinforcement learning for environment interaction. It advances integrated big data modeling theory and technology, specifically addressing theoretical and technical challenges arising from massive-scale, high-dimensional, heterogeneous, and dynamic characteristics in big data, while developing cross-disciplinary methodologies combining mathematics, informatics, statistics, and computer science for real-world domain applications.
B. Intelligent Optimization and Decision Systems
This domain focuses on complex challenges—such as multi-objective uncertainties, highly nonlinear hybrid characteristics, and modeling difficulties—encountered in signal processing, production scheduling, task allocation, pattern recognition, automatic control, and mechanical design. It investigates AI-integrated methodologies for such problems, develops data-driven intelligent optimization modeling theories, advances multi-dimensional and multi-scale machine learning-based optimization/ decision-solving approaches, and addresses prevalent engineering challenges.
C. Image Processing and Machine Vision
Focusing on challenges in equipment manufacturing—including massive data volumes, redundant information, and high-dimensional feature spaces—this domain employs mathematical foundations, pattern recognition, and machine learning integrated with computer technologies, digital image processing, object detection/tracking, non-contact measurement, and deep learning. It advances industrial AI theories and technologies for measurement, recognition, and tracking, prioritizing research in:
Imaging, perception, and decision-making in vision systems
Multimodal fusion, perception, and modeling
Intelligent inspection and non-contact measurement
Multi-target visual tracking in complex environments
Behavioral monitoring and environmental perception for autonomous driving
Multi-agent collective perception and collaboration
III. Course Info
Artificial Intelligence: Theory and Applications, Foundations and Applications of Machine Learning, Neural Networks and Reinforcement Learning, Pattern Recognition: Theory and Implementation, Data Mining, Machine Vision and Applications, Digital Image Processing, Natural Language Processing with Applications, Python for Data Analysis and Practice, etc.
IV. Program Educational Objectives
Aligned with China's national economic development and industrial innovation needs, this program cultivates well-rounded, practice-innovation talents in artificial intelligence who demonstrate integrated development in moral, intellectual, physical, aesthetic, and labor education. Graduates will achieve the following competencies:
A. Patriotism and politically sound orientation; heightened social responsibility; professional integrity and dedication; adherence to academic ethics; physical and mental wellness.
B. Strong theoretical foundation and specialized knowledge in AI; mastery of advanced technical methods and modern engineering tools for solving domain-specific problems; familiarity with relevant industry standards.
C. Design-implementation-testing capabilities for AI systems within a specialization area, demonstrating practical innovation.
D. Professional foreign language proficiency with organizational, coordination, technical negotiation, and international communication skills.
