Continuous time state-space model identification with application to magnetic bearing systems

This thesis presents the identification of continuous time linear multi-variable systems using state-space models. A data-driven approach in realization by the subspace methods is carried out in developing the models. In this thesis, the approach by subspace methods is considered for both open-loop and closed-loop continuous time system identification. The Laguerre filter network, the instrumental variables and the frequency sampling filters are adopted in the framework of subspace model identification. More specifically, the Laguerre filters play a role in avoiding problems with differentiation in the Laplace operator, which leads to a simple algebraic relation. It also has the ability to cope with noise at high frequency region due to its orthogonality functions. The instrumental variables help to eliminate the process and measurement noise that may occur in the systems. The frequency sampling filters are used to compress the raw data, eliminate measurement noise so to obtain a set of clean and unbiased step response data. The combination of these techniques allows for the estimation of high quality models, in which, it leads to successful performance of the continuous time system identification overall. The application based on a magnetic bearing system apparatus is used to demonstrate the efficacy of the proposed techniques

Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation

The control of nonlinear dynamical systems remains a major challenge for autonomous agents. Current trends in reinforcement learning (RL) focus on complex representations of dynamics and policies, which have yielded impressive results in solving a variety of hard control tasks. However, this new sophistication and extremely over-parameterized models have come with the cost of an overall reduction in our ability to interpret the resulting policies. In this paper, we take inspiration from the control community and apply the principles of hybrid switching systems in order to break down complex dynamics into simpler components. We exploit the rich representational power of probabilistic graphical models and derive an expectation-maximization (EM) algorithm for learning a sequence model to capture the temporal structure of the data and automatically decompose nonlinear dynamics into stochastic switching linear dynamical systems. Moreover, we show how this framework of switching models enables extracting hierarchies of Markovian and auto-regressive locally linear controllers from nonlinear experts in an imitation learning scenario.Comment: 2nd Annual Conference on Learning for Dynamics and Contro