This course will introduce the students to the fundamental concepts and methods in modern control, especially optimal control and estimation theory. Topics include state-space modelling of dynamical systems, least square estimation and system identification, state-feedback and output-feedback controller design, optimal control, dynamic programming, Model predictive control, linear quadratic regulators, and Kalman filter. The course will also connect these control and estimation methods to applications in robotics, mechanical, electrical, and aerospace systems.
本课程将向学生介绍现代控制的基本概念和方法，特别是最优控制和估计理论。主题包括动态系统的状态空间建模、最小二乘估计和系统辨识、状态反馈和输出反馈控制器设计、最优控制、动态规划、模型预测控制、线性二次调节器和卡尔曼滤波器。 本课程还将把这些控制和估计方法与机器人、机械、电气和航空航天系统的应用联系起来。
- 2024 Spring
2024春季学期的实验课，主要包含了RNN，Regressor，Path Planning，Dynamic Programming，LQR以及EKF卡尔曼滤波器。
Project主要有三个，分别是：
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project 1：预测天气、使用最小二乘进行位置估计、机械手状态估计
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project 2：LQR控制倒立摆
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Project 3：二足机器人的状态估计、强化学习控制倒立摆、使用LQR控制一阶倒立摆
- 2024 Spring, including the course codes, assignment codes, and projects: Ethylene9160/SDM366_Optimal_Estimation: Assignments on cource SDM366Optimal Control and Estimation, sustech spring 2024. (github.com)