A model-free tuning method for proportional-multi-resonant controllers
Charles Lorenzini, Lu\'is Fernando Alves Pereira, Alexandre Sanfelice, Bazanella, Gustavo R. Gon\c{c}alves da Silva

TL;DR
This paper introduces a simple, model-free tuning method for proportional-multi-resonant controllers that relies on a single frequency response measurement, making controller design easier and more accessible for various linear plants.
Contribution
The paper presents a novel, easy-to-implement tuning approach for resonant controllers that does not require detailed plant models or complex calculations.
Findings
Method successfully tuned controllers in three example cases
Approach simplifies the tuning process similar to Ziegler-Nichols methods
Applicable to general linear time-invariant plants
Abstract
Resonant controllers are widely used in applications involving reference tracking and disturbance rejection of periodic signals. The controller design is typically performed by a trial-and-error approach or by means of time and resource-consuming analytic methods that require an accurate plant model, intricated mathematics and sophisticated tools. In this paper, we propose an easily implementable, model-free method for tuning a proportional-multi-resonant controller applicable to general linear time-invariant causal plants. Just like the Ziegler-Nichols methods, the proposed methodology consist in identifying one specific point of the plant's frequency response -- which is easily obtained in a relay with adjustable phase experiment -- and then designing the controller with simple tuning formulas and tables. The method is analyzed in detail for three examples, showing its practical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Control Systems Design · Iterative Learning Control Systems · Adaptive Control of Nonlinear Systems
