Description

This course provides an in-depth overview of several post-modern and non-linear control concepts and methods that are applicable to a wide range of deterministic and stochastic dynamical systems. The following topics are covered in the course: review of state estimation and modern control theory, mathematical models, input-state and input-output feedback linearization, iterative learning control, evolutionary algorithms, artificial neural networks, dynamic programming, model predictive control, reinforcement learning, and relationship of reinforcement learning to model predictive control and optimal control.

Prerequisites

ENGS 26; ENGS 145 is recommended