  In this paper, a multi-objective model-following control problem is solved
using an observer-based adaptive learning scheme. The overall goal is to
regulate the model-following error dynamics along with optimizing the dynamic
variables of a process in a model-free fashion. This solution employs an
integral reinforcement learning approach to adapt three strategies. The first
strategy observes the states of desired process dynamics, while the second one
stabilizes and optimizes the closed-loop system. The third strategy allows the
process to follow a desired reference-trajectory. The adaptive learning scheme
is implemented using an approximate projection estimation approach under mild
conditions about the learning parameters.
