Machine Learning: A New ICE (Identification, Control, Estimation) Age ?

Описание: Talk by Alberto Bemporad at the IFAC 2020 Workshop on "Machine Learning meets Model-based Control", July 11, 2020.

Slides: http://cse.lab.imtlucca.it/~bemporad/...

ABSTRACT:
Control theory always evolved by taking full advantage of developments in other disciplines, for example frequency-domain methods have leveraged on complex analysis, state-space approaches on linear algebra, optimization-based analysis and synthesis on linear matrix inequalities, model predictive control (MPC) on numerical optimization (quadratic/nonlinear/mixed-integer programming). In recent years, a variety of different approaches and advanced software tools were developed by the machine learning community and have been proved amazingly successful in many application domains. It is therefore likely that they will have a strong impact also in the field of systems and control, enabling the development of a whole new set of tools for identification, control, and estimation (ICE) of dynamical systems. In my talk I will provide evidence of how machine learning tools can be used to develop new control design methods
by reviewing some results recently obtained by my research group, including the use of artificial neural networks for MPC based on quadratic or mixed-integer programming, stochastic gradient descent methods for optimal policy search, reinforcement learning for MPC, Bayesian optimization for MPC auto-tuning, and preference-learning methods for semi-automatic calibration of MPC systems.