Howard Jay Chizeck
E E 549
Fundamentals of state estimation for linear and nonlinear systems. Discrete and continuous systems. Probability and stochastic systems theory. Models with noise. Kalman-Bucy filters, extended Kalman filters, recursive estimation. Numerical issues in filter design and implementation. Prerequisite: either A A 547, E E 547, or M E 547. Offered: jointly with A A 549/M E 549.
A great many control design and analysis applications involve systems that are not well-understood, and for which detailed models are unavailable. Without precise models, most of the methods of state-space control theory are not applicable. In these situations, and in situations where systems are slowly time varying, the methods of system identification and adaptive control are appropriate. These techniques have, for the most part, been designed for computer implementation - hence they are most often developed and applied to discrete time systems (or sampled continuous-time systems).
This course approaches topics of system identification, estimation and adaptive control through an input/output approach. The course will focus almost entirely on discrete time formulations, and will address both theoretical topics (algorithm convergence, stability) and implementation issues. The course will include a project - with students working in small groups.
The goal of this course is to enable all students to have the skills and knowledge to successfully apply identification and adaptive control methods.
Student learning goals
General method of instruction
Class assignments and grading