3 research outputs found
A Python package for the Virtual Reference Feedback Tuning, a direct data-driven control method
In this paper, thepyvrft, a Python package for the data-driven control method known as Virtual Reference Feedback Tuning (VRFT), is presented. Virtual Reference Feedback Tuning is a control designtechnique that does not use a mathematical model from the process to be controlled. Instead, it uses input and output data from an experiment to compute the controller’s parameters, aiming to minimizean H2 Model Reference criterion. The package implements an unbiased estimate of the controller for MIMO (Multiple-Input Multiple-Output) processes using both least-squares and instrumental variabletechniques. The package also provides accessory functions to import data and to perform MIMO systems simulations, together with some examples
Virtual reference feedback tuning for linear discrete-time systems with robust stability guarantees based on set membership
In this paper we propose a novel methodology that allows to design, in a
purely data-based fashion and for linear single-input and single-output
systems, both robustly stable and performing control systems for tracking
piecewise constant reference signals. The approach uses both (i) Virtual
Reference Feedback Tuning for enforcing suitable performances and (ii) the Set
Membership framework for providing a-priori robust stability guarantees.
Indeed, an uncertainty set for the system parameters is obtained through Set
Membership identification, where an algorithm based on the scenario approach is
proposed to estimate the inflation parameter in a probabilistic way. Based on
this set, robust stability conditions are enforced as Linear Matrix Inequality
constraints within an optimization problem whose linear cost function relies on
Virtual Reference Feedback Tuning. To show the generality and effectiveness of
our approach, we apply it to two of the most widely used yet simple control
schemes, i.e., where tracking is achieved thanks to (i) a static feedforward
action and (ii) an integrator in closed-loop. The proposed method is not fully
direct due to the Set Membership identification. However, the uncertainty set
is used with the only objective of providing robust stability guarantees for
the closed-loop system and it is not directly used for the performances
optimization, which instead is totally based on data. The effectiveness of the
developed method is demonstrated with reference to two simulation examples. A
comparison with other data-driven methods is also carried out