Adaptive Velocity Estimators for Learning Control

The paper proposes a method for learning velocity estimators, in the form of finite impulse response (FIR) filters, from data collected from a system equipped with quantizing position encoders that is to be controlled by means of a full-state feedback controller making use of the velocity estimates. The resulting estimators are tailored to the properties of the controlled system and show empirically superior performance in comparison with commonly used baseline velocity estimators, both in terms of velocity estimation error as well as in terms of reduced regulation cost when tested on control problems. The proposed adaptive estimators are resistant to overfitting the training data, are easy to implement on embedded controller devices, and can be used in conjunction with various learning control methods.