學門簡介

控制學門概況

「控制工程學門」是工程處目前19個學門中的一個學門，主要在於協助國科會工程處，積極推動控制工程相關研究發展之規劃與各項業務，主要目的為推動國內一般研究計畫，及產學合作研究計畫，整合培養堅強之研發團隊，厚植國家工業實力，且提升國家學術地位。控制工程學門共包含以下八個研究分項：

  1. 控制與決策理論（Control and Decision Theory）：基本的控制理論發展需要較嚴謹數學理論與推導的各種傳統控制理論之創新與突破，從線性系統延伸至非線性系統的各種先進控制理論與應用聚焦於純粹的控制理論，包含：量子控制（Quantum Control）、切換系統（Switched Systems）、滑動模式控制（Sliding Mode Control）、描述子系統（Descriptor Systems）、時延系統（Time-Delay Systems）、重複性學習控制（Iterative Learning Control）、彈性控制系統 (Resilient Control Systems)、事件觸發控制(Event-triggered Control)、能量函數之建構與控制應用(Energy Function Construction and Control Applications)、數據驅動控制(Data-Driven Control)、估測器與濾波器設計(Estimator and Filter)、強韌控制(Robust Control)、狀態限制控制 (State Constraints Control)、決策理論 (Decision Theory) (金融工程、SLAM, 行人軌跡預測，GPS 定位，音源定位等決策演算法)等子題。
  2. 人工智慧系統與控制應用（Artificial Intelligence Systems and Control Applications）：人工智慧已成為新世代科技，人工智慧控制與應用亦已成為全球控制領域的研究重點之一，人工智慧控制乃需人工智慧演算法與控制理論相結合，才能廣泛地應用於各類系統的控制問題，並以設計智慧型控制器或控制演算法，達成控制之目的。本子題研究涵蓋：(1)   模糊控制系統創新設計與應用、(2)   類神經網路控制、(3)智慧型控制學習演算法、(4)人工智慧控制在智慧機械與智慧製造之應用、(5)智慧網路控制系統應用與物聯網資訊安全、(6)機電系統智慧控制。
  3. 人工智慧感知與推理（Artificial Intelligence Perception and Reasoning）：隨著深度學習、生成式模型、多模態技術與大語言模型的迅速發展，人工智慧系統已逐步具備模擬人類感知、理解與推理的能力。人工智慧感知與推理的核心包含單模態到融合多模態甚至跨模態(如影像、語音、文字與感測訊號) 之智慧感知，結合符號式、統計式或大語言模型推理模型，進而推動智慧系統從「辨識」邁向「理解」進而「推論」。研究領域包含：(1) 單模態(如影像)智慧感知之各類AI模型研究。(2) 多模態感知融合(影像、語音、語意、行為、感測器與環境的多感測異質融合)、(3) 視覺語言與生成式推理模型(VLMs、VLA、LLM、RAG技術等)、(4) 資料與知識驅動推理、(5) 可解釋AI與可信任AI模型設計、(6) 人類意圖與行為推理(動態軌跡與意圖預測、駕駛決策推論、人機協同理解)、(7) 感知與推理於智慧應用之整合(智慧機器人與人機協作、無人載具、智慧製造、智慧醫療與照護決策支援系統等)。
  4. 系統整合與工業應用（System Integration and Industrial Application）：系統整合與工業應用研究主要旨在將不同的技術、流程整合在系統上，以實現高效、自動化及智慧化之成果。常見利用控制、人工智慧、大數據、物聯網、最佳化演算法、量測儀表、感測器、通信等技術，聚焦於生產過程、監測設備、優化效能及數據分析和決策支援，進而創造產業貢獻與影響力。研究領域包含：(1)精密動態控制、(2)智慧物聯網與系統、(3)智慧製造系統與應用、(4)系統監控與診斷、(5)視覺伺服與控制技術。
  5. 智慧生醫照護及系統生物控制 （Intelligent Biomedical Healthcare and Systems Biology Control）: 生物醫學相關科技研究已是二十一世紀世界最熱門的研究重點，本子題從穿戴式醫療照護系統、系統生物學、與民生用品控制三個方向進行規劃，包含：(1)穿戴式醫療照護系統（先進穿戴式生理量測裝置開發、人工智慧神經回饋之技術發展與應用、臨床神經性疾病預測分析與輔助診斷應用、標準化醫資系統整合、數據隱私和安全、使用者體驗和可接受性驗證法）、(2)系統生物學（系統生物學在精準醫學的應用、計算系統生物學、精準醫學中的資料整合與解釋、疾病機制的系統化解釋、計算系統生物學的網路模型發展、AI在醫療保健與預防醫學的應用）、(3)民生用品控制技術（智慧家居產品的開發與跨平台、可穿戴設備的能源和續航力、智慧交通的城市規劃和管理、能源的智慧管控、智慧醫療的數據隱私和安全、新型態照護型民生用品的開發、亞健康族群的軟硬體應用）。
  6. 智慧機器人（Intelligent Robots）:  隨著全球勞動力短缺，人口老化與低薪屈就意願等因素，全球從工業到醫療、服務、教育與軍事等各領域對機器人之需求均持續高漲。而隨著人工智慧之發展，機器人所需之環境感知、電腦視覺、自主能力與智慧化程度均快速推進，加速機器人與自動化產業的發展進程，為人類創造更加便利、高效和安全的生活及工作環境。研究領域包含：(1)機器人感測系統與人機互動（機器人環境感測與融合、機器人人機互動、演示學習機器人系統）、(2)特殊應用機器人（飯店與餐廚服務機器人、救援與防疫機器人、陪伴與寵物機器人）、(3)工業機器人（工業應用機器人、機器人自動化技術、虛實整合、人機協同操作、高效節能減碳優化技術）、(4)醫療與行動輔助機器人（復健與行動輔助機器人、手術機器人、生理訊號操控機器人）、(5)特殊結構機器人（仿生機器人、複合式機器人、人形機器人、群組機器人、旋翼機器人）、(6)機器人之雲端資源共享與管理（基於雲端計算之機器人學習系統、雲端資源管理最佳化機制）。
  7. 智慧載具（Intelligent Light Mobility）:無人載具包括陸海空的自動駕駛車，水域與飛行。研究領域包含: (1)陸域載具（無人自駕車、智聯車群(Pseudo linked vehicles)、無人巴士、電助力腳踏車、電動輪椅、智慧電動摩托車、ADAS核心技術、識別與感測技術、服務機器人、保全機器人、穿戴式載具、醫療與復健載具、AGV、農用機、車用載具之無線電力傳輸）、(2)水域載具（水下自動控制載具AUV、仿生機械魚型載具、水下燃料電池驅動之載具設計與應用）、(3)空域載具（多旋翼無人機控制與應用、農用飛航機、運輸物流飛航機、大型電動飛機與飛行船）、(4)多用途載具開發（水陸兩用載具、陸空兩用載具、陸海空三用載具）、(5)載具核心支持技術(自主定位與先進導航)
  8. 永續淨零與能源控制 (Sustainable Net-Zero and Energy Control): 藉由自動控制科技減少碳足跡，規劃碳捕捉、再利用與封存系統，發展新興能源與永續資源循環控制有其必要性。此外，電動載具的環保特性使其成為綠能運輸重要來源，故發展電動載具之電源控制與智慧節能系統，亦成為永續淨零控制重要研究議題。研究範圍包含：再生能源控制、前瞻儲能系統、碳捕捉與封存及再利用、新興能源與永續資源循環控制、電動載具綠能運輸、節能技術。

 

本學門現任召集人為國立中興大學電機工程學系 莊家峯教授，下設有三個委員會，由學門召集人負責邀請，

工程處同意後之相關專長教授或業界專家擔任委員

  1. 一般計畫複審委員：約15位，負責計畫之分發與複審。開會時間約在每年的二月及四月。
  2. 規劃委員：約20位，負責學門重點或前瞻研究的規劃。開會時間約在三月~五月，規劃一次，大約訂出學門三年的研究方向。
  3. 產學計畫複審委員：約10位，負責大小產學計畫之分發與複審。開會時間約在每年的三月及十月。

 

Introduction to the Control Engineering Program, Department of Engineering and Technologies, National Science and Technology Council

The 'Control Engineering Program' is one of the research programs under the Department of Engineering and Technologies of National Science and Technology Council (NSTC), Taiwan. Its main purpose is to assist the Department of Engineering and Technologies of the NSTC in promoting the planning and various tasks related to the development of control engineering researches. The primary goal is to promote general research projects and industry-academia collaborative research projects in the country, integrate and cultivate strong research and development teams, strengthen the national industrial capabilities, and enhance the nation's academic status. The Control Engineering Program comprises the following eight research subfields:

 

1. Control and Decision Theory: Innovations and breakthroughs in fundamental control theories, from rigorous mathematical theory to various advanced control theories and applications, including Quantum Control, Switched Systems, Sliding Mode Control, Descriptor Systems, Time-Delay Systems, Iterative Learning Control, Resilient Control Systems, Event-triggered Control, Energy Function Construction, and Control Applications, Data-Driven Control, Estimator and Filter Design, Robust Control, State Constraints Control, and Decision Theory (algorithms for financial engineering, SLAM, pedestrian trajectory prediction, GPS localization, sound source localization, etc.).

 

2. Artificial Intelligence Systems and Control Applications: Artificial intelligence has become a focus of research in the control field worldwide. This subfield combines artificial intelligence algorithms with control theory to address control problems in various systems. Research areas include: (1) Fuzzy Control System Design and Applications, (2) Neural Network Control, (3) Intelligent Control Learning Algorithms, (4) Applications of Artificial Intelligence in Smart Manufacturing and Industrial Internet of Things, (5) Smart Network Control System Applications, and IoT Information Security, and (6) Smart Mechatronics Control.

 

3.Artificial Intelligence Perception and Reasoning: With rapid advances in deep learning, generative models, multimodal technologies, and large language models (LLMs), AI systems have increasingly gained the capability to emulate human perception, understanding, and reasoning. The core of AI perception and reasoning spans intelligent perception across single-modality, multimodal, and even cross-modal data (e.g., images, speech, text, and sensor signals). By integrating symbolic, statistical, or LLM-based reasoning models, AI systems are progressing from recognition toward understanding and ultimately inference. The research areas include: (1) Development of AI models for single-modality intelligent perception (e.g., vision-based models). (2) Multimodal perception fusion, such as heterogeneous integration of images, speech, semantics, behaviors, sensors, and environmental information. (3) Vision-language and generative reasoning models (e.g., VLMs, VLA, LLMs, RAG-based techniques). (4) Data-driven and knowledge-driven reasoning methodologies. (5) Explainable AI and trustworthy AI model design. (6) Human intent and behavior reasoning, such as dynamic trajectory and intent prediction, driver decision inference, and human–machine collaborative understanding. (7) Integration of perception and reasoning for intelligent applications, such as intelligent robots and human-robot collaboration, unmanned vehicles, smart manufacturing, and intelligent medical and healthcare decision-support systems.

4. System Integration and Industrial Application: Research in system integration and industrial applications aims to integrate different technologies and processes to achieve efficiency, automation, and intelligence in various industries. It commonly involves control, artificial intelligence, big data, the Internet of Things, optimization algorithms, measurement instruments, sensors, and communication technologies. Research areas include: (1) Precision Dynamic Control, (2) Smart IoT and Systems, (3) Smart Manufacturing Systems and Applications, (4) System Monitoring and Diagnosis, and (5) Visual Servo and Control Technologies.

5. Intelligent Biomedical Healthcare and Systems Biology Control: Research in this subfield covers three main directions: wearable medical care systems, systems biology, and control technologies for consumer goods. Topics include: (1) Advanced Wearable Physiological Measurement Device Development (Application of Artificial Intelligence and Neural Feedback, Predictive Analysis and Assistive Diagnosis of Clinical Neurological Diseases, Standardization and Integration of Medical Information Systems, Data Privacy and Security, User Experience, and Acceptance Verification Methods), (2) Systems Biology in Precision Medicine (Computational Systems Biology, Data Integration and Interpretation in Precision Medicine, Systematic Interpretation of Disease Mechanisms, Network Model Development in Computational Systems Biology, and Applications of AI in Healthcare and Preventive Medicine), as well as (3) Control Technologies for New Types of Consumer Goods for Sub-Healthy Populations.

 

6. Intelligent Robots: With the global shortage of labor, aging populations, and low-wage tolerance, the demand for robots in various fields, from industry to healthcare, service, education, and the military, continues to rise. Advancements in artificial intelligence have accelerated the development of robots, making them more capable of environmental perception, computer vision, autonomy, and intelligence, thus contributing to more convenient, efficient, and safe living and working environments. Research areas include: (1) Robot Sensing Systems and Human-Machine Interaction, (2) Specialized Robot Applications (hotel and restaurant service robots, rescue and epidemic prevention robots, companion and pet robots), (3) Industrial Robots (industrial robot applications, robot automation technology, virtual and real integration, human-machine collaborative operation, energy-efficient and carbon reduction optimization technology), (4) Medical and Mobile Assistive Robots (rehabilitation and mobile assistive robots, surgical robots, physiologically controlled robots), (5) Special Structure Robots (bio-inspired robots, composite robots, humanoid robots, group robots, rotorcraft robots), and (6) Cloud-based Resource Sharing and Management for Robots (cloud-based robot learning systems, cloud resource management optimization mechanisms).

 

7. Intelligent Light Mobility: Unmanned vehicles include automated land, sea, and air vehicles. Research areas encompass: (1) Land Vehicles (unmanned self-driving vehicles, pseudo linked vehicle fleets, unmanned buses, electric-assisted bicycles, electric wheelchairs, smart electric motorcycles, core technologies of ADAS, identification and sensing technologies, service robots, security robots, wearable vehicles, medical and rehabilitation vehicles, AGVs, agricultural machinery, wireless power transmission for vehicle use), (2) Sea Vehicles (autonomous underwater vehicles AUVs, bio-inspired mechanical fish-type vehicles, underwater fuel cell-driven vehicle design and applications), (3) Air Vehicles (multi-rotor unmanned aerial vehicles and applications, agricultural flight vehicles, transportation and logistics flight vehicles, large electric aircraft and airships, rocket-powered space application vehicles), (4) Multipurpose Vehicle Development (amphibious vehicles, land, sea, and air vehicles), and (5) Vehicle Core Support Technologies (high-efficiency motor design, power electronics technology related to motor drives, battery and energy-related safety control).

 

8. Sustainable Net-Zero and Energy Control: The reduction of carbon footprint through automatic control technology, planning for carbon capture, reuse and storage systems, and the development of emerging energy and sustainable resource recycling control are essential. Additionally, the environmental-friendly features of electric vehicles make them an important source of green energy transportation. Therefore, the development of electric vehicle power control and smart energy-saving systems is also a crucial research topic. Research areas include Renewable Energy Control, Advanced Energy Storage Systems, Carbon Capture and Storage, Emerging Energy and Sustainable Resource Recycling Control, Green Energy Transportation with Electric Vehicles, and Energy-saving Technologies.