Control of Stochastic Systems
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Displaying course information from Spring 2013.
||1530 - 1650
|| T R
||241 Everitt Lab
||Mohamed Ali Belabbas
||Stochastic control models; development of control laws by dynamic programming; separation of estimation and control; Kalman filtering; self-tuning regulators; dual controllers; decentralized control. Course Information: Prerequisite: ECE 515 and ECE 534.
||Credit in ECE 515
Credit in ECE 534
|Detailed Description and Outline
- Introduction: decision-making under uncertainty
- Markov chain models: structure, steady-state probabilities, transience and recurrence, Lyopunov functions and stability
- State space models: state, observation and control processes
- Properties of linear stochastic systems: linear Gaussian systems; asymptotic properties, Gauss-Markov processes; quadratic costs
- Controlled Markov chain models: finite state systems; Markov and stationary policies; cost of Markov policy; infinite state systems
- Input-output models: elimination of state variables; impulse response and frequency response models
- Dynamic programming: optimal control laws; complete and partial information; information state, dual control
- Estimation and control of linear stochastic systems: linear Gaussian systems; Kalman filter; optimal linear-quadradic control; minimum variance control for input-output models
- Identification and adaptive control: Bayesian and non-Bayesian approaches; maximum likelihood estimate; least-squares, prediction error and instrumental variable methods; recursive identification; ODE method; self-tuning regulator
P.R. Kumar and P. Varaiya, Stochastic Systems, Prentice-Hall, 1986.
Last updated: 2/13/2013