Identification of Linear Control Systems via Gradient Descent

In this dissertation we use gradient descent and its variations, in the spirit of machine learning to identify a linear control system. When the full state is observable, the most natural least square cost function is convex. However, when the state is partially observable, this is no longer the case. We propose two algorithms for later problem and show that the cost function decreases as the iteration proceeds. The simulations are provided to support that theoretical results. We also perform recursivity analysis when the amount of data increases. Finally we provide an asymptotic analysis when a certain natural parameter goes to infinity.