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Fall 2012:
Course#: 18879C/96840SV
Optimal Control: Theory and Applications
- Breadth Area: Artificial Intelligence, Robotics and Control
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Meeting Information:
Lecture info:
Times: Tues/Thurs 10AM-11:20AM (PST); Fri 1-2:20PM (PST)
Location: SV: Rm. 118; PGH HH 1107
Lectures will be available online via adobe connect: http://cmusv.adobeconnect.com/optimalcontrol/
TA/Review Sessions:
TBD
Course description
This course will cover the fundamentals optimal control theory and include applications from current research in aeronautics. Specific topics include: extrema of functions and functionals with and without constraints, Lagrange multipliers, calculus of variations, variational approach to optimal control with and without constraints, interior points, bang-bang control, LQR, Pontryagin maximum principle, Dynamic programming and the Hamilton-Jacobi-Bellman equations, singular optimal control, and stochastic optimal control.
Prerequisites: This course is intended for advanced undergraduate and beginning graduate students. The prerequisites are ordinary differential equations and 18-470 – Fundamentals of Control. It is helpful, but not required, to have taken or to take concurrently: 18-771 – Linear Systems which is currently offered by Professor Sinopoli without conflict. While the course will utilize elements of real and functional analysis, prior exposure to these topics it is not required. However, students with such previous exposure will manifestly find the material more digestible. At the conclusion of the course, stochastic control may be covered and the essential elements from probability theory will be developed as needed.
Textbook
Liberzon, D. (2012). Calculus of variations and optimal control theory: A concise introduction. Princeton, N.J: Princeton University Press.
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Spring 2012:
Adaptive Control and Signal Processing
- Breadth Area: Artificial Intelligence, Robotics and Control
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Course syllabus (.pdf) |
Meeting Information:
Lecture Times (1.5 hrs):
Monday 1:30 AM (PT) /4:30 PM (ET)
Friday 11:30 AM (PT) /2:30 PM (ET)
TA/Review Sessions (1hr):
Wednesday at 11:30 AM (PT) /2:30 PM (ET)
Location:
Lecture: SV: Rm. 118 CM SV Campus; PGH HH 1107
TA/Review Sessions: Rm. 118 CM SV Campus; PGH HH D210
Course description
This course will cover fundamentals of learning and adaption for control system design and signal processing. Both continuous and discrete time systems will be considered.
Topics include:
- Overview of adaptive controls
- Real-time parameter estimation
- Self-tuning regulators
- Vector spaces; function spaces; norms; Lyapunov stability analysis
- Model reference adaptive control
- Adaptive robot control
- Adaptive backstepping control
- Learning algorithms for digital filters
- Quadratic performance functions and speed of convergence
- Adaptive signal processing applications
- Adaptive inverse control
Textbook
Astrom, K.J., and B. Wittenmark, Adaptive Control, Addison-Wesley, Reading, Massachusetts, 2nd edition, 1995
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Spring 2010:
Neural Network Control of Nonlinear Systems
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- Assignments:
- Lecture Notes
- Review Session Notes:
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