State Space Predictive Minimum Variance Controller Applied to a Tacho Generator Motor

This work proposes the project of the State Space Predictive Minimum Variance Controller, this is applied to a Tacho Generator Motor, whose system was assembled and used as a way of evaluating identification, modeling and control techniques. The proposed work is based on the principles of generalized minimum variance control, with a state space approach, in order to analyze the convergence of the system using a state observer with estimated gains with the Observer-Kalman Filter Estimation. In addition to using Reinforcement Learning to tune the control weighting factor of the system.