ECE 555
ECE 555 - Control of Stochastic Systems
Fall 2024
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Control of Stochastic Systems | ECE555 | C | 43280 | LEC | 4 | 1100 - 1220 | T R | 3020 Electrical & Computer Eng Bldg | Mohamed Ali Belabbas |
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Official Description
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.
Subject Area
- Control Systems
Course Director
Description
Stochastic control models; development of control laws by dynamic programming; separation of estimation and control; Kalman filtering; dual controllers.
Topics
- 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
Detailed Description and Outline
Topics:
- 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
Texts
P.R. Kumar and P. Varaiya, Stochastic Systems, Prentice-Hall, 1986.
Last updated
2/13/2013