EE402 DISCRETE TIME SYSTEMS
Course Code: | 5670402 |
METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
ECTS Credit: | 5.0 |
Department: | Electrical and Electronics Engineering |
Language of Instruction: | English |
Level of Study: | Undergraduate |
Course Coordinator: | Assoc.Prof.Dr. MUSTAFA MERT ANKARALI |
Offered Semester: | Spring Semesters. |
Course Objectives
Course Objective 1: Students will be able to comprehend basic systems and signals representations: in continuous space as well as discrete space
Course Objective 2: Students will be able to assess Transient response of LTI and be able to know the effect of transient parameters for design
Course Objective 3: Students will be able to analyze stability and design a controller
Course Objective 4: Students will be able to design compensators in frequency domain
Course Objective 5: Students will be able to model systems in state space and analyze controllability and observability
Course Objective 6: Students will be able to estimate states and use it for state feedback
Course Content
Importance and advantages of discrete time system models in control. Time domain analysis of discrete-time systems. Sampled data systems. Stability; translation of analog design. State space design methods: observer theory, introduction to optimal design methods. Quantization effects.
Course Learning Outcomes
For Objective 1
1.1 Manipulating Z Transform,its inverse
1.2 Relating Complex Operations to Laplace and Z Transforms
1.3 Sampling of signals; effect of sampling to transfer functions;
1.4 reconstruction of continuous data from sampled ones
For Objective 2
2.1 Transient responses of LTI systems and transient parameters
2.2 Steady state error and its relation to system characteristics
2.3 System modeling in discrete time and in Z domain
For Objective 3
3.1 using PD,PI,PID controllers and tuning their parameters according to the design specs and stability characteristics.
3.2 using root locus and tuning parameters according to the transient parameters required in the specs and characteristics of relative stability.
For Objective 4
4.1 system characteristics represented by Bode Plots, Phase margin, gain margin
4.2 Design of lag compensators for required system relative stability and transients based on gain margin, phase margin
4.3 Design of lead compensators for required system relative stability and transients based on gain margin, phase margin
4.4 Design of lead lag compensators for required system relative stability and transients based on gain margin, phase margin
4.5 When to use lead ;when to use lag and what Lead lag provides.
For Objective 5
5.1 Modeling discrete systems in state space and analyzing the model sensitivity to systems parameters
5.2 analyzing controllability and observability in different spaces: normalized systems and nondecoupled systems
For Objective 6
6.1 design with state feedback
6.2 estimate state by designing an observer
6.3 Use estimated state in pole placement: use the state feedback in conjunction with observer design
Program Outcomes Matrix
Contribution | |||||
# | Program Outcomes | No | Yes | ||
1 | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | ✔ | |||
2 | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | ✔ | |||
3 | An ability to communicate effectively with a range of audiences | ✔ | |||
4 | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts. | ✔ | |||
5 | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | ✔ | |||
6 | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | ✔ | |||
7 | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | ✔ |