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Course Detail

Course Name Control System
Course Code 23AID305
Program B.Tech in Artificial Intelligence and Data Science
Semester 5
Credits 3
Campus Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati

Syllabus

Unit 1

Introduction to Control Systems and Frequency Domain Methods, Definition and types of control systems, Mathematical modelling of systems, Block diagram and signal flow graph representation of systems, Feedback and control system characteristics, Stability analysis and root locus techniques, Frequency response analysis, Bode plot and Nyquist plot, PID controllers Lead-lag compensators, Design of classical controllers using root locus.

Unit 2

State Space Methods State space analysis and design, State-Space representation of control systems: state variables, state-space models, Multivariable control systems: MIMO systems, decoupling, Controllability and observability, Pole placement and observer design, Linear quadratic regulator (LQR) Optimal control, Introduction to nonlinear control

Unit 3

Applications of Control Systems, Control of mechanical systems, Control of electrical systems, Control of chemical and biological systems, Introduction to optimal control for aerospace system

Objectives and Outcomes

Course Objectives

  • This course introduces the fundamentals of control systems through a hands-on approach involving programming tools such as MATLAB.
  • This course familiarizes concepts of control systems, such as open-loop, closed-loop, and feedback systems.
  • This course enables the students to judge the performance and stability of control systems

Course Outcomes

After completing this course, students will be able to

CO1

Explain the fundamental principles that govern control systems.

CO2

Apply analytical techniques to evaluate and characterize basic control systems.

CO3

Evaluate the performance and stability of control systems

CO4

Apply control system theory to practical applications in engineering.

CO-PO Mapping

PO/PSO

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

PSO1

PSO2

PSO3

CO

CO1

3

3

2

1

3

–

–

–

3

3

–

2

3

2

1

CO2

3

3

3

2

3

1

–

–

3

3

–

2

2

2

1

CO3

3

3

3

2

3

–

–

–

3

3

–

2

2

2

1

CO4

3

3

3

2

3

1

–

–

3

3

–

3

3

2

1

Evaluation Pattern

Evaluation Pattern

Assessment

Internal/External

Weightage (%)

Assignments (Minimum 3)

Internal

30

Quiz(Minimum 2)

Internal

20

Mid-Term Examination

Internal

20

Term project/End semester examination

External

30

Text Books / References

Text Books / References

  1. L. Brunton and J. N. Kutz, “Data-driven science and engineering: Machine learning, dynamical systems, and control”, Cambridge University Press, 2022. ISBN 9781108422093.

Ogata Katsuhiko, “Modern control engineering”, Prentice Hall, 2010. ISBN 9780136156734.

  1. F. Golnaraghi, B. C. Kuo, and M. F. Golnaraghi, “Automatic control systems” Wiley, 2010. ISBN9780470048962.
  2. Nise, “Control systems engineering”, 6th ed. John Wiley & Sons, 2017. ISBN 9780470917695.
  3. F. Franklin, J. D. Powell, and M. L. Workman, “Digital control of dynamic systems”, Vol. 3, Ellis Kagle Press, 1998. ISBN 9780979122606.

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