Optimal Control and Estimation
MAE 546
Princeton University
School of Engineering and Applied Science
Department of Mechanical and Aerospace Engineering
Spring 2013
Tuesday and Thursday, 3-4:20 pm
J-201, Engineering Quadrangle
Designing control logic that commands a dynamic system to a desired output or that augments the system's stability is a common objective in many technical fields, ranging from decision-making for economic and social systems through trajectory control for robots and vehicles to the development of optimal therapeutic protocols for treating disease. If the control objective can be expressed as a quantitative criterion, then optimization of this criterion establishes a feasible design for control logic. This course addresses the theory and application of optimal control, including the effects of uncertain inputs (i.e., disturbances) and measurement error.
Titles of Previous Term Papers
- Approximate Output Tracking Using Nonlinear Cost Minimization for Non-Minumum Phase CTOL Aircraft Model
- Estimation and Control of a Low-Order Model of Transitional Channel Flow
- Estimation of Foot Reaction Forces of a Running Cockroach
- Study of Optimal Control for Nuclear Reactors
- Stochastic Optimal Control of Resistive Wall Mode in a Tokamak
- Optimal Control and Estimation of a Firing Neuron
- Kalman Filter Applications for Multiconjugate Adaptive Optics
- Time-Optimal Controller for Multiple Vehicle Velocity and Position Placement in the Phase Plane
- Optimal Estimation of the Communication Graph in Multi-Agent Consensus Systems
- Optimal Video Tracking of Multiple Fish with Kalman Filters
- Optimal Control and Estimation of a Deformable Mirror using Two-Actuators
- Trajectory Optimization for Multi-Agent Collision Avoidance
- Optimal Control of a Two-Strain Tuberculosis Epidemic
- Time-of-Day Pricing for Internet Service Providers: Streaming Sessions
- Optimal Control and Estimation for a UAV Helicopter
- Control of Output Trajectories in Networks of Phase Oscillators using an ANN Mode-Based Predictive Algorithm
- Identification and Control of an HIV Dynamic Model Using a State-Dependent Linear-Quadratic Controller and Nonlinear Estimation
The primary reference for this course is OPTIMAL CONTROL AND ESTIMATION, R. F. Stengel, Dover Publications, 1994.
Additional books that are useful for reference include:
- Anderson, B., and Moore, J., Optimal Control: Linear-Quadratic Methods, Prentice Hall, 1990.
- Brogan, W., Modern Control Theory, Prentice-Hall, 1991.
- Bryson , A. E., Jr., and Ho, Y. C., Applied Optimal Control, Hemisphere, 1975.
- Dickinson, B., Systems: Analysis, Design, and Computation, Prentice Hall, 1991.
- Gelb, A., ed., Applied Optimal Estimation, MIT Press, 1974.
- Graham, A., Kronecker Products and Matrix Calculus: with Applications, J. Wiley, 1981.
- Grantham, W., and Vincent, T., Modern Control Systems Analysis and Design, Wiley, 1993.
- Hull, D., Optimal Control Theory for Applications, Springer-Verlag, 2003.
- Kwakernaak, H., and Sivan, R., Linear Optimal Control Systems, Wiley, 1972.
- Maciejowski, J., Multivariable Feedback Design, Addison-Wesley, 1989.
- Maybeck, P., Stochastic Models, Estimation, and Control, Academic Press, 1982.
- Skelton, R., Dynamic Systems Control, Wiley, 1988.
- Zhou, Z., Doyle, J., and Glover, K., Robust and Optimal Control, Prentice Hall, 1996.
