Stochastic Systems

(3-0-0-3)

CMPE Degree: This course is Not Applicable for the CMPE degree.

EE Degree: This course is Not Applicable for the EE degree.

Lab Hours: 0 supervised lab hours and 0 unsupervised lab hours.

Technical Interest Group(s) / Course Type(s): Systems and Controls

Course Coordinator:

Prerequisites: CEE/ISYE/MATH 3770

Catalog Description

Advanced techniques in stochastic analysis with emphasis on stochastic
dynamics, nonlinear filtering and detection, stochastic control and
stochastic optimization and simulation methods.

Textbook(s)

Course Outcomes

Not Applicable

Strategic Performance Indicators (SPIs)

Not Applicable

Topical Outline

1. Introduction
a. Stochastic dynamic problems in control and robotics
b. Review basic probability
2. Probability and stochastic analysis
a. Random walks and Brownian motion
b. Stochastic integrals and martingales
c. Stochastic differential equations and Ito calculus
3. Stochastic Systems
a. Stochastic stability
b. Evolution equation: Kolmogorov and Fokker-Planck
4. Optimal Estimation
a. Static estimation: linear and nonlinear
b. Explicit solutions and approximations
5. Stochastic Control
a. Optimal stochastic control: Full information case
b. Optimal stochastic control: Partial information case
c. Separation principle and LQG-design
d. Dynamic programming, value functions, verification theorem
6. Applications in Robotics and Control
a. Sensor measurements
b. Localization and sensor fusing
c. SLAM