Fictitious Reference Iterative Tuning Research Articles

VIMT of non‐minimum phase system by using initial control signal

AbstractIn this paper, input‐oriented virtual internal model tuning (VIMT) is expanded to non‐minimum phase systems. Proposed method can tune the feedback controller and the desired transfer function. Compared to Fictitious Reference Iterative Tuning (FRIT), the proposed method can realize good controllers even when the time constant of the desired transfer function is small. The validity of the proposed method is verified via numerical simulation.

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

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

MPC for Artificial Muscles Using FRIT based Optimized Pseudo Linearization Model

A McKibben-type artificial muscle exhibits strong asymmetric hysteresis characteristics. In a previous study, fictitious reference iterative tuning (FRIT) was applied to a 1-DOF rotational actuator applying these muscles because it was able to determine the controller parameters using only a pair of single closed-loop data. However, it is generally difficult to design a reference model properly without information on the plant, which is strongly related to the control performance, and sometimes this leads to performance degradation. To solve this problem, in this study, we propose a method combining FRIT and model predictive control (MPC). First, we define the pseudo linearization (PL) model, which is adopted as a predictor in MPC, and is used to optimization between the model and design a control system by matching the closed-loop system to the PL model using FRIT. Additionally, it can consider input constraints by combined MPC.

Tuning of Hypnosis Controller Based on FRIT for Wiener Systems with Dead Time

Many studies of automatic anesthesia control systems have been done to maintain patients at an appropriate anesthetic state during surgery. Since individual differences in the response of a patient to anesthetic drug are quite large, the controller must be adjusted for each patient to achieve an appropriate and safe anesthesia control. This paper proposes a tuning method using the Fictitious Reference Iterative Tuning (FRIT) of a controller for Wiener systems with dead time, i.e., a class of systems consisting of a linear dynamics followed by a dead time and a memoryless nonlinearity. We then apply the method to a hypnosis controller and explore a suitable setting of the initial parameters of the controller to discuss the effectiveness of the proposed method through numerical experiments for patients with individual differences.

Intelligent Proportional Controller Tuned by Virtual Reference Feedback Tuning and Fictitious Reference Iterative Tuning

A comparative study of two mixes of data-driven algorithms is proposed in this paper. The discrete-time second-order model-free control known as an intelligent Proportional (iP) controller is tuned considering Virtual Reference Feedback Tuning (VRFT) and the overall performance of the resulting algorithm iP-VRFT is compared with that of iP controller that is tuned considering Fictitious Reference Feedback Tuning (FRIT), an algorithm that is further referred as iP-FRIT. The comparison is carried out as far as the control systems with these two control algorithms are concerned. The purpose of the comparison study is to determine if VRFT and FRIT optimally determine the iP parameters such that both control system structures perform in the same manner. Both algorithm mixes are experimentally validated on the Tower Crane System equipment.

Database‐driven practical online adjustment of PID parameters

AbstractThe database‐driven proportional‐integral‐derivative (PID) control method, which is an online adjustment method of PID parameters for nonlinear systems, has a long calculation time and is difficult to apply to industrial machines. In this paper, a novel practical online adjustment method for PID parameters is proposed. The proposed method can be applied to industrial machines while adopting the database‐driven approach. According to the proposed method, the overall calculation cost can be reduced by performing the online adjustment operation of the PID parameters in a slow cycle different from the calculation of the control output. In addition, PID parameters can be learned offline manner using the one‐shot experimental data group corresponding to each shot. The learning method can adopt the extended fictitious reference iterative tuning (E‐FRIT), which calculates control parameters directly from closed‐loop data. Then, the database is updated while maintaining a certain amount of database size. The learned data are added to the existing database, and similar data are discarded using the cluster analysis method. Finally, the effectiveness of the proposed method is verified by a numerical simulation of the time‐variant system. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Anaerobic Digestion Processes Controller Tuning Using Fictitious Reference Iterative Method

In this paper, a new data-based procedure, Fictitious Reference Iterative Tuning, is proposed to control the anaerobic digestion process. In the first phase, the proposed approach uses input-output data from the anaerobic digestion process obtained by using a controller with initial parameters that ensure loop stability. In the second phase, the situation in which the input-output data are obtained in a closed-loop was also analyzed. Therefore, the Fictitious Reference Iterative Tuning method was used to obtain: a PI controller, which was tuned on the basis of an iterative, convergent and monotonous process and a PID controller, which was tuned on the basis of a divergent iterative process. The results obtained confirm the validity of the proposed Fictitious Reference Iterative Tuning method for the control of the anaerobic digestion process.

Data‐Driven Controller Tuning for Unmeasurable Outputs to Achieve Desired Tracking Properties

Conventional parameter‐tuning methods, such as virtual reference feedback tuning and fictitious reference iterative tuning (FRIT), are not applicable to plants where the variables to be controlled cannot be measured. In this work, we propose a new method of controller parameter tuning for this class of systems. In the proposed method, the controller can be tuned using one‐shot experimental observation and manipulation data. The proposed method can help tune the controller parameters without the plant model by using a manipulated variable and an observed variable. The validity of the proposed methods was also verified by a motor speed‐control experiment. The experimental results show that the proposed method can help realize good control parameters without observing the controlled variable. Compared to FRIT, the proposed method has the following advantages: the closed‐loop response is very close to the desired response even if the output signals cannot be observed. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Synthesis and a Tuning Based on FRIT of Smith Compensator for Multi-input and Multi-output Systems with Input Delay and Output Delay

In this paper, we consider a controller tuning method of the Smith compensator for multi-input and multi-output (MIMO) systems with both input delays and output delays. Particularly, we discuss on the application of Fictitious Reference Iterative Tuning (FRIT), which is one of the data-driven controller update methods utilizing one-shot experimental data. To apply FRIT to systems where both input and output have time delays, we introduce a specific structure of the Smith compensator with tunable parameters. In addition, we consider the meaning of the cost function used in the proposed method. Finally, we illustrate a numerical example to show the validity and the effectiveness of the proposed method.

Tuning of Hypnosis Controller from Data During Anesthesia Induction Using FRIT

Abstract Many studies of automatic anesthesia control systems to maintain an appropriate anesthetic state during surgery have been done. Since individual difference in the response of a patient to anesthetic drugs is quite large, the controller must be adjusted for each patient to achieve an appropriate anesthesia control. In our pilot study, we applied the fictitious reference iterative tuning (FRIT) method to tuning of a proportional-integral-derivative (PID) controller in a hypnosis control system for in silico patients with individual differences in gain and another parameter and obtained an appropriate controller from 7-minute data during anesthesia induction in most cases. In this paper, we apply the method to in silico patients with individual differences in pharmacodynamic parameters and dead-time, and evaluate the effectiveness of the method.

Application of a MIMO-PID Controller for a Hydraulic Excavator Considering the Velocity of CoM

There are many machines that require human operation in industry, and high operational skills are required to operate these machines efficiently. However, the number of highly skilled workers is decreasing due to the recent trends of falling birthrate and population aging. This decline is particularly pronounced in the construction industry, while the demand for construction workers remains high owing to the increasing number of developed infrastructures. To reduce this mismatch between the supply and the demand, it is important to achieve high efficiency in tasks using hydraulic excavators, because these machines can greatly increase the productivity at construction sites. Accordingly, it is necessary to improve productivity even if unskilled operators use hydraulic excavators. This paper proposes a control system that achieves efficient motions based on the velocity of the center of mass (CoM) of the hydraulic excavator’s attachments, which reflects the characteristics of skilled workers’ operations. The motions of multiple attachments give rise to interference terms owing to the characteristics of the hydraulic system. A two-input two-output control system, in which the input consists of the lever input and the output is the CoM velocity is constructed. The fictitious reference iterative tuning (FRIT) method is used to calculate the controller parameters. The proposed method was verified by comparing the results of a simulated digging motion and an experiment with an actual hydraulic excavator operated by an unskilled operator.

Design of a Data-Driven Two-Degree-of-Freedom Control System Considering Robustness

Virtual reference feedback tuning (VRFT) and fictitious reference iterative tuning (FRIT) are the data-driven tuning schemes for directly designing feedback controllers. These schemes have been extended to two-degree-of-freedom (2DOF) control systems in recent years. The conventional design schemes of 2DOF controllers need a complicated two-stage tuning process. This paper describes a one-stage tuning scheme for the 2DOF control system using a fictitious exogenous signal based on a data-driven method. In the proposed scheme, 2DOF controllers are designed with one criterion, and the least squares method is applied to find the optimal set of 2DOF controller parameters. According to the proposed scheme, the reference models are determined based on the stability margin that is quantified by the sensitivity function. It is also possible to design a control system taking robustness into account. Finally, the effectiveness of the proposed scheme is numerically verified using a simulation example.

Design of a PID Controller using a Fictitious Exogenous Signal for a Fluctuation System

The fictitious reference iterative tuning method is used to design Proportional—Integral—Derivative (PID) controllers to achieve desired performance for industrial machines. However, the desired control performance cannot be obtained by this method, if the system characteristics changed in steady state. In this paper, design method of a PID controller using the Fictitious Exogenous Signal is proposed. The change of system characteristics is considered as an impulse-like virtual disturbance. The Fictitious Exogenous Signal to calculate PID parameters is obtained from a set of operational data. The effectiveness of the proposed method has been verified by numerical examples.

Performance-driven Control System using Off-line Learned Recurrent Neural Networks

To maintain high performance of controlling and minimize the total cost for readjustments of the controller parameters, determination the appropriate timing for readjustment the controller parameters is important. This paper proposes a design of performance-driven control system to maintain the system performance by detecting the system change using the recurrent neural networks and readjusting the control parameters based on Fictitious Reference Iterative Tuning (FRIT). This paper conducts some numerical experiments to verify the availability of the proposed method to some systems.

Data‐driven minimum variance control using regulatory closed‐loop data based on the FRIT method

AbstractThe paper proposes data‐driven minimum variance regulatory control using a Fictitious Reference Iterative Tuning (FRIT) method. Our previous work has extended the original FRIT for reference signal tracking to the case of a stochastic disturbance attenuation problem. However, the method parametrizes the controller by using plant parameters based on a specific controller structure for minimum variance control. The present work uses a linearly parametrized inverse of the controller. Thus, a general parametrization is realized, and the integrator is straightforwardly introduced. An analytical result for the data‐driven cost function is also given. A numerical example shows effectiveness of the proposed method.

Design of a Data-Driven Control System using a Multi-Objective Genetic Algorithm

In recent years, control design schemes for directly calculating control parameters from operational data have been realized and include the virtual reference feedback tuning (VRFT) method and the fictitious reference iterative tuning (FRIT) method. They were designed for objects that have a linear system. However, many objects in industry are nonlinear; hence, it is challenging to obtain good control performance by only applying fixed PID controllers. In this study, multiple linear systems as objects using multiple linear controllers are investigated. Specifically, it is necessary to solve two optimization problems of (i) the number of controllers (ii) the control parameters of each controller, and it is solving by using multi-objective genetic algorithm (MOGA) in this research.

Data-driven Minimum Variance Control Using Regulatory Closed-loop Data Based on the FRIT Method

The paper proposes data-driven minimum variance regulatory control using a Fictitious Reference Iterative Tuning (FRIT) method. The present work uses the inverse of a linearly parametrized controller. Thus, a general parametrization is realized, and the integrator is straightforwardly introduced. A numerical example shows an effectiveness of the proposed method.

Data-Driven Impedance Matching in Multilateral Teleoperation Systems

<span lang="EN-US">This paper addresses a tuning approach to improve transparency between master and slave manipulators in a multilateral teleoperation system. The slave manipulators comprised of multi-agents of simple mass are converted into a bilateral system through the passive decomposition technique. There are two main operations being considered, namely; grasping and handling. The object grasping is achieved through consensus control. For handling, an equalizer constructed by a Laguerre function connected in-feedback-loop to the master manipulator is chosen and tuned to achieve impedance matching between both sides of teleoperation. Fictitious Reference Iterative Tuning (FRIT) is adopted to properly tune the selected equalizer. The result shows that by introducing an optimally tuned equalizer, the overall impedance matching and transparency between the single master and multi-slave manipulators are improved. Finally, the effectiveness of the proposed tuning algorithm is presented through a numerical example.</span>

Design and Experimental Evaluation of a Discrete‐time ASPR‐based Adaptive Output Feedback Control System Using FRIT

AbstractThis paper describes the design of an adaptive output feedback control system in discrete‐time, based on almost strictly positive real (ASPR)‐ness with a feedforward input. It is well‐known that an adaptive output feedback control system based on ASPR conditions can achieve asymptotic stability via a constant feedback gain. Unfortunately, most realistic systems are not ASPR because of the severe conditions. The introduction of a parallel feedforward compensator (PFC) is an efficient way to alleviate such restrictions. However, the problem remains that there exists a steady state error between the output of the augmented system and the output of the original system. The proposed scheme provides a strategy wherein the feedforward input is utilized such that the steady state error is removed. Furthermore, the fictitious reference iterative tuning (FRIT) approach is employed to determine the control parameters using one‐shot input/output experimental data directly, without prior information about the control system. This paper explains how the FRIT approach is applied in designing an adaptive output feedback control system. The effectiveness of the proposed scheme is confirmed experimentally, by using a motor application.

Design of a Data-Oriented PID controller for a Two Degree of Freedom Control System

Virtual Reference Feedback Tuning (VRFT) and Fictitious Reference Iterative Tuning (FRIT) are the data-oriented tuning schemes for directly designing feedback controllers. In these schemes, controller parameters are tuned to achieve the desired response without any system identifications. Furthermore, these schemes have been extended to the two-degree of freedom (2DOF) control systems in recent years. The conventional design schemes of 2DOF controllers are a two-step tuning method. The first step is to design a feedback controller to satisfy the desired disturbance response. The second step is to tune the feed-forward controller parameters to achieve the desired reference response. Consider these methods are complicated, this paper provides a one-step tuning scheme for a 2DOF control system based on data-oriented method. According to the proposed scheme, the reference of the complementary sensitivity function is introduced, and the least squares method can be applied to calculate the optimal controller parameters. Finally, the effectiveness of the proposed scheme is verified by using a numerical example.