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ABET Course Objectives and Outcomes Form

Course number and title: EE142 Linear Systems: State-Space Approach
Credits: 4
Instructor(s)-in-charge: N. Levan (levan@ee.ucla.edu)
Course type: Lecture
Required or Elective: A pathway course.
Course Schedule:
Lecture: 3 hrs and 40 mins/week. Meets twice weekly.
Dicussion: 1 hr/discussion section. Multiple discussion sections offered per quarter.
Outside Study: 7 hrs/week.
Office Hours: 2 hrs/week by instructor. 2 hrs/week by teaching assistant.
 
Course Assessment:
Homework: 8 assignments.
Exams: 1 midterm and 1 final examination.
 
Grading Policy: Typically 25% homework, 25% midterm, 50% final.
Course Prerequisites: EE102.
Catalog Description: State-space methods of linear system analysis and synthesis, with application to problems in networks, control and system modeling.  
Textbook and any related course material:
J. S. Bay, Fundamentals of Linear State Space Systems, McGraw-Hill, NY, 1999.
 
Course Website
Additional Course Website
Topics covered in the course and level of coverage:
Mathematical models of physical systems in the form of ordinary differential equations and difference equations, examples. 2 hrs.
Solutions of linear ordinary differential and difference equations with constant coefficients. 2 hrs.
Mathematical background on linear algebra: vector spaces, basis, linear transformations and their matrix representations, eigenvectors, eigenvalues and singular values of linear transformations, simple linear transformations. 5 hrs.
Linear dynamic systems described by ordinary differential and difference equations and their matrix representations with respect to a given basis,state transition matrix and its properties. 6 hrs.
Linear time-invariant dynamic systems, matrix exponential and its computation, canonical forms, modal representations, dominant modes, transfer functions. 4 hrs.
Controllability, observability of linear continuous and discrete-time systems and their canonical forms. 6 hrs.
Introduction to observers. 2 hrs.
Stability of linear time-invariant continuous and discrete-time systems. 2 hrs.
Feedback stabilization of linear time-invariant continuous and discrete-time systems. 1 hrs.
Course objectives and their relation to the Program Educational Objectives:  
Contribution of the course to the Professional Component:
Engineering Topics: 0 %
General Education: 0 %
Mathematics & Basic Sciences: 0 %
Expected level of proficiency from students entering the course:
Mathematics: Strong
Physics: Some
Chemistry: Not Applicable
Technical writing: Some
Computer Programming: Some
Material available to students and department at end of course:
  Available to
students
Available to
department
Available to
instructor
Available to
TA(s)
Course Objectives and Outcomes Form: X X X X
Lecture notes, homework assignments, and solutions: X X
Samples of homework solutions from 2 students: X
Samples of exam solutions from 2 students: X
Course performance form from student surveys: X X
Will this course involve computer assignments? YES Will this course have TA(s) when it is offered? YES

  Level of contribution of course to Program Outcomes
(a) Strong  
(b) Average  
(c) Average  
(i) Average  
(m) Some  
(n) Some  
Strong: (a)
Average: (b) (c) (i)
Some: (m) (n)

:: Upon completion of this course, students will have had an opportunity to learn about the following ::
  Specific Course Outcomes Program Outcomes
1. Understand the notion of �state� of a dynamical system, and formulate mathematical models of physical systems described by ordinary differential and difference equations in state-variable form. a
2. Develop linear state equations from nonlinear state equations via linearization. a m
3. Obtain explicit solutions for systems of linear ordinary differential and difference equations with constant coefficients. a m n
4. Understand clearly the basic concepts and results, and acquire the computational skills in linear algebra that are relevant to system theory. a m n
5. Derive various canonical forms of linear time-invariant continuous and discrete-time systems and understand their significance in system modeling and design. a m n
6. Understand clearly the notions of controllability, observability and stabilizability. a m n
7. Determine controllability and observability for linear time-invariant continuous and discrete-time systems. a m n
8. Construct controllability and observability canonical forms for linear time-invariant systems, and understand their significance in system modeling and design. a m n
9. Determine controllability and observability in complex systems formed by interconnecting linear time-invariant subsystems. a m n
10. Design observers for estimating linear functions of the state of linear time-invariant systems. a m n
11. Solve completely the feedback stabilization or pole-placement problem for linear time-invariant systems. a m n
12. Several homework assignments delving on basic concepts and reinforcing analytical skills learned in class. a
13. A mini-project on applying the theory developed in class to models of real-world systems. Software such as MATLAB is used for computer simulation studies. b c
14. Opportunities to interact weekly with the instructor and the teaching assistant(s) during regular office hours and discussion sections in order to further the students' learning experience and the students' interest in the material. i

  Program outcomes and how they are covered by the specific course outcomes
(a)   Understand the notion of �state� of a dynamical system, and formulate mathematical models of physical systems described by ordinary differential and difference equations in state-variable form.  
  Develop linear state equations from nonlinear state equations via linearization.  
  Obtain explicit solutions for systems of linear ordinary differential and difference equations with constant coefficients.  
  Understand clearly the basic concepts and results, and acquire the computational skills in linear algebra that are relevant to system theory.  
  Derive various canonical forms of linear time-invariant continuous and discrete-time systems and understand their significance in system modeling and design.  
  Understand clearly the notions of controllability, observability and stabilizability.  
  Determine controllability and observability for linear time-invariant continuous and discrete-time systems.  
  Construct controllability and observability canonical forms for linear time-invariant systems, and understand their significance in system modeling and design.  
  Determine controllability and observability in complex systems formed by interconnecting linear time-invariant subsystems.  
  Design observers for estimating linear functions of the state of linear time-invariant systems.  
  Solve completely the feedback stabilization or pole-placement problem for linear time-invariant systems.  
  Several homework assignments delving on basic concepts and reinforcing analytical skills learned in class.  
(b)   A mini-project on applying the theory developed in class to models of real-world systems. Software such as MATLAB is used for computer simulation studies.  
(c)   A mini-project on applying the theory developed in class to models of real-world systems. Software such as MATLAB is used for computer simulation studies.  
(i)   Opportunities to interact weekly with the instructor and the teaching assistant(s) during regular office hours and discussion sections in order to further the students' learning experience and the students' interest in the material.  
(m)   Develop linear state equations from nonlinear state equations via linearization.  
  Obtain explicit solutions for systems of linear ordinary differential and difference equations with constant coefficients.  
  Understand clearly the basic concepts and results, and acquire the computational skills in linear algebra that are relevant to system theory.  
  Derive various canonical forms of linear time-invariant continuous and discrete-time systems and understand their significance in system modeling and design.  
  Understand clearly the notions of controllability, observability and stabilizability.  
  Determine controllability and observability for linear time-invariant continuous and discrete-time systems.  
  Construct controllability and observability canonical forms for linear time-invariant systems, and understand their significance in system modeling and design.  
  Determine controllability and observability in complex systems formed by interconnecting linear time-invariant subsystems.  
  Design observers for estimating linear functions of the state of linear time-invariant systems.  
  Solve completely the feedback stabilization or pole-placement problem for linear time-invariant systems.  
(n)   Obtain explicit solutions for systems of linear ordinary differential and difference equations with constant coefficients.  
  Understand clearly the basic concepts and results, and acquire the computational skills in linear algebra that are relevant to system theory.  
  Derive various canonical forms of linear time-invariant continuous and discrete-time systems and understand their significance in system modeling and design.  
  Understand clearly the notions of controllability, observability and stabilizability.  
  Determine controllability and observability for linear time-invariant continuous and discrete-time systems.  
  Construct controllability and observability canonical forms for linear time-invariant systems, and understand their significance in system modeling and design.  
  Determine controllability and observability in complex systems formed by interconnecting linear time-invariant subsystems.  
  Design observers for estimating linear functions of the state of linear time-invariant systems.  
  Solve completely the feedback stabilization or pole-placement problem for linear time-invariant systems.  

:: Last modified: February 2013 by J. Lin ::

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