Iterative Feedback Tuning of Model-Free Controllers

Baciu, Andrei; Lazăr, Corneliu; Caruntu, Constantin F.

doi:10.1109/icstcc52150.2021.9607213

Cited by 5 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven control is the other option. Iterative Feedback Tuning (IFT) [3] and Correlation-based Tuning (CbT) [4] have both been explored and applied in academia and industry as one-step tuning methods, as well as multi-step tuning methods such as Iterative Learning Control (ILC) and Virtual Reference Feedback Tuning (VRFT). The performance of a system is not significantly affected by model accuracy when using these data-driven tuning methods.…”

Section: Introductionmentioning

confidence: 99%

Lqr-based PID controller with variable load tuned with data-driven methods for double inverted pendulum

Aslam,

Bilal,

Hayajneh

2023

Soft Comput

View full text Add to dashboard Cite

The modern controller design is increasingly based on data-driven control methods. PID controllers are still used primarily in the industry because they are proportional-integral-derivative (PID). The Linear Quadratic Regulator (LQR) provides an efficient way to tune the parameters of a PID controller. The LQR has the disadvantage of requiring accurate models of the system, as well as reducing a high-order system to a secondorder model. Firstly, the authors explore the new horizons of control theory in the form of the state-space model for high nonlinear systems. Secondly, the Lagrangian method establishes the new mathematical model of the linear two-stage inverted pendulum system. In addition, real-time LQR parameters are calculated under the variable load. This method has been demonstrated to be highly applicable and accurate through simulations and experiments using the Double Inverted Pendulum model.

show abstract

Section: Introductionmentioning

confidence: 99%

Lqr-based PID controller with variable load tuned with data-driven methods for double inverted pendulum

Aslam,

Bilal,

Hayajneh

2023

Soft Comput

View full text Add to dashboard Cite

show abstract

“…One such combination involves the data-driven VRFT algorithm resulting in the MFC-VRFT combination, where the VRFT algorithm optimally determines the parameters of the MFC algorithm after considering an initial open-loop experiment (Roman et al, 2016a;Precup et al, 2021b). Another MFC improvement was made by combining the MFC algorithm with IFT resulting in the so-called P(iP)-IFT algorithm (Baciu et al, 2021) designed in the framework of optimal control. The data-driven Fictitious Reference Iterative Tuning (FRIT) algorithm (Soma et al, 2004) is distinguished by the fact that it uses the initial I/O data collected after an initial closed-loop experiment and after several iterations returns the optimal parameters of the controller whose parameters are calculated.…”

Section: Introductionmentioning

confidence: 99%

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

ROMAN¹,

PRECUP²,

PETRIU³

et al. 2023

SIC

View full text Add to dashboard Cite

The purpose of this paper is to propose a novel controller that is based on a combination of two data-driven algorithms, namely the Fictitious Reference Iterative Tuning (FRIT) algorithm and the Model-Free Adaptive Control (MFC) algorithm while considering a particular form of MFC, that is the intelligent proportional-integral-derivative (iPID) controller. The main advantage of this combination is that the FRIT algorithm optimally tunes the parameters of the iPID controller by solving an optimization problem based on a metaheuristic African Vultures Optimization Algorithm (AVOA). The novel controller, referred to as the FRIT-iPID controller, is validated experimentally on a three-degree-of-freedom tower crane system laboratory equipment in the context of controlling the cart position, the arm angular position and the payload position for this system.

show abstract

LQR-Based PID Controller with Variable Load Tuned with Data-Driven Methods for Double Inverted Pendulum.

Aslam

Zhao

2022

Preprint

View full text Add to dashboard Cite

The modern controller design is increasingly based on data-driven control methods. PID controllers are still used primarily in the industry because they are proportional-integral-derivative (PID). The Linear Quadratic Regulator (LQR) provides an efficient way to tune the parameters of a PID controller. The LQR has the disadvantage of requiring accurate models of the system, as well as reducing a high–order system to a second-order model. Firstly, the authors explore the new horizons of control theory in the form of the state-space model for high nonlinear systems. Secondly, the Lagrangian method establishes the new mathematical model of the linear two–stage inverted pendulum system. In addition, real–time LQR parameters are calculated under the variable load. This method has been demonstrated to be highly applicable and accurate through simulations and experiments using the Double Inverted Pendulum model.

show abstract

Iterative Feedback Tuning of Model-Free Controllers

Cited by 5 publications

References 7 publications

Lqr-based PID controller with variable load tuned with data-driven methods for double inverted pendulum

Lqr-based PID controller with variable load tuned with data-driven methods for double inverted pendulum

Fictitious Reference Iterative Tuning of Discrete-Time Model-Free Control for Tower Crane Systems

LQR-Based PID Controller with Variable Load Tuned with Data-Driven Methods for Double Inverted Pendulum.

Contact Info

Product

Resources

About