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2020　Robot Dynamics and Control

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Academic unit or major
Undergraduate major in Mechanical Engineering
Instructor(s)
Okada Masafumi 
Class Format
Lecture    (ZOOM)
Media-enhanced courses
Day/Period(Room No.)
Thr5-8(S222,S223)  
Group
-
Course number
MEC.I333
Credits
2
Academic year
2020
Offered quarter
4Q
Syllabus updated
2020/10/1
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course is organized by modern control theory and robot control method. At first, representation and analysis of a linear system are explained based on state-space representation. After that, as a nonlinear system, robot control methods are explained.

Student learning outcomes

By the end of this course, students will be able to;
(1) Modern control system
(a) Represent a linear system by a state-space formulation
(b) Assess controllability and observability of the linear system
(c) Design a controller based on pole assignment, linear quadratic regulator and observer.
(2) Robot control
(a) Understand kinematics and statics of a manipulator
(b) Understand dynamics of a manipulator
(c) Design a compliance controller

Keywords

Modorn control theory, kinematics, statics, dynamics

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills
✔ This class aims at learning 6 and 7 of learning objective.

Class flow

This course is mainly organized lectures. Since each lecture requires the knowledge of the previous lecture, the students have to well review the previous lessons.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Dynamic equation and state-space equation Obtain State-space equation of a linear system
Class 2 Transfer function and state-space equation Transfer transfer function into state-space equation, state-space equation into transfer function
Class 3 Stability analysis of state-space equation Assess the stability of the system based on eigen values of state transition matrix
Class 4 Connection of systems and minimum realization Calculate minimum realization from transfer function
Class 5 Controllability and observability Assess controllability and observability of state-space equation
Class 6 State feedback and pole assignment Design a state feedback controller based on pole assignment theory
Class 7 Optimal control Design an optimal controller using Linear quadratic regulator
Class 8 Observer and Kalman filter Estimate state value using observer theory
Class 9 Kinematics and coordinates transformation Understand robot kinematics
Class 10 Euler angle and orientation Represent link orientation using Euler angle
Class 11 Inverse kinematics and Newton-Raphson method Obtain a solution of inverse kinematics using Newton-Raphson method
Class 12 Statics and Virtual work principal Obtain a solution of statics using Virtual work principal
Class 13 Forward/inverse dynamics Obtain a solution of forward/inverse dynamics based on dynamic equation
Class 14 Linearization of nonlinear systems，Compliance control, Impedance control Linearize a nonlinear system based on Taylor expansion or linearized feedback，Design Compliance controller and Impedance controller

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None

Reference books, course materials, etc.

None

Assessment criteria and methods

Evaluate by reports.

Related courses

  • MEC.I312 ： Modeling and Control Theory
  • MEC.I211 ： Robot Kinematics
  • MEC.I332 ： Exercise in Mechatronics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students should have completed "MEC.I211 Robot Kinematics" and "MEC.I312 Modeling and Control Theory" or have equivalent knowledge.

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