國立中興大學教學大綱

課程名稱	(中) 線性系統(6717)
	(Eng.) Linear Systems
開課單位	機械系
課程類別	選修	學分	3	授課教師	李聯旺
選課單位	機械系 / 碩士班	授課使用語言	中文	開課學期	1142
課程簡述	This course is designed mainly for graduate students as a continuation of Automatic Control Systems. In Automatic Control Systems, we learned about classical control design methods, such as the root-locus, Bode plot, and the Nyquist criterion. In this course, we will focus on the so-called “modern control” techniques and associated data-driven methods, aiming to equip students with mathematical modeling, system identification, and practical control system design skills. It covers state-space modeling techniques, stability analysis, controllability and observability, state feedback design, estimator design, Linear Quadratic Regulator (LQR), and Model Predictive Control (MPC), as well as intelligent and data-driven control technologies for complex systems. The following topics will be covered in this course: 1. Learn mathematical modeling and state-space analysis methods for systems. 2. Understand and master techniques for analyzing system stability, controllability, and observability. 3. Design and apply state feedback and output feedback control methods. 4. Understand and apply convex optimization methods in control system design. 5. Master advanced control methodologies from modern control (LQR) to data-driven methods (such as MPC, neural networks, and fuzzy control). 6. Develop skills in literature review, practical project planning, and presentation of practical outcomes.
先修課程名稱

課程與核心能力關聯配比(%)

課程目標之教學方法與評量方法

課程目標

核心能力

配比(%)

教學方法

評量方法

The following topics will be explored in depth throughout this course:
1. Mathematical Foundations: State-space modeling and mathematical description of systems.
2. System Analysis: State-space solutions, realizations, and stability analysis.
3. Core Concepts: Theoretical frameworks and applications of controllability and observability.
4. Controller Design: Synthesis of state-feedback, estimators, and output-feedback controllers.
5. Optimization: Application of convex optimization techniques to control problems.
6. Advanced Methodologies: From Linear Quadratic Regulator (LQR) to Model Predictive Control (MPC).
7. Complex Systems: Complex adaptive systems theory, data-driven modeling, and control.
8. Modern Integration: Fuzzy control, neural network control, Dynamic Mode Decomposition (DMD), and Physics-Informed Neural Networks (PINN).
9. Emerging Techniques: Data-driven adaptive iterative learning control and extremum seeking control.

Upon completing this subject, students will acquire the mathematical proficiency required to analyze the stability, robustness, and output performance of linear dynamical systems. Furthermore, they will develop the expertise to design high-performance estimators, feedback compensators, and advanced control strategies—including LQR, MPC, and data-driven intelligent technologies—to effectively regulate and optimize the aforementioned system properties for complex engineering applications.

1.具備機械領域之專業知識與獨立解決問題之能力。

2.具備創新思考、規劃並執行研究專題及表達研究成果之能力。

3.具協調跨領域人才與整合技術之能力。

專題探討/製作

習作

討論

講授

作業

口頭報告

測驗

出席狀況

授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習)

週次	授課內容
第1週	Mathematical Descriptions of Systems Writing State-Space Models
第2週	State-Space Solutions and Realizations
第3週	Stability Controllability and Observability
第4週	State Feedback and Estimator Design
第5週	Output Feedback Design
第6週	Convex Optimization
第7週	State Space Design – Linear Quadratic Regulator (LQR)
第8週	Report: Implementation Plan for Journal Articles Related to Course Content
第9週	From LQR to Model Predictive Control (MPC)
第10週	Complex Adaptive Systems Theory – An overview of Data-Driven Modeling Technology and Control
第11週	Identification and Control of Complex Systems – Model Identification Based on Fuzzy and Neural Networks
第12週	Identification and Control of Complex Systems – Combination and application of dynamic mode decomposition (DMD) and physics-informed neural network (PINN)
第13週	Intelligent control of complex dynamic systems (from fuzzy logic, fuzzy inference to controller parameter adjustment)
第14週	Data-driven adaptive iterative learning control
第15週	Extremum Seeking Control (ESC) – Data-driven/model-free
第16週	Report: Demonstrating Practical Outcomes of Journal Article Applications
自主學習內容	03.製作專題報告

學習評量方式

Attendance: 10%
Homework Assignments: 20%
Midterm Oral Presentation: 30% (Implementation Plan for Course-Related Journal Articles)
Final Oral Presentation: 40% (Practical Demonstration of Journal Article Applications)

教科書＆參考書目(書名、作者、書局、代理商、說明)

Chi-Tsong Chen, Linear System Theory and Design,Oxford University Press 1999 New York,USA

課程教材（教師個人網址請列在本校內之網址）

數位教學平台iLearning

課程輔導時間

週五 PM：2:10-3:00

聯合國全球永續發展目標(連結網址)

09.工業、創新基礎建設

提供體驗課程：N

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更新日期西元年/月/日：2026/02/22 13:31:53	列印日期西元年/月/日：2026 / 3 / 28
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