Virtual Reference Feedback Tuning Approach Research Articles

Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study

Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.

Robust cascade controller design for discrete minimum phase system using a novel data-driven virtual reference feedback tuning approach

Robust cascade controller design for discrete minimum phase system using a novel data-driven virtual reference feedback tuning approach

Robust cascade controller design for discrete minimum phase system using a novel data-driven virtual reference feedback tuning approach

Robust cascade controller design for discrete minimum phase system using a novel data-driven virtual reference feedback tuning approach

A novel robust Virtual Reference Feedback Tuning approach for minimum and non-minimum phase systems

In real-world applications, it is often desired that the design of a closed-loop system must attain not only high performance but also robustness. This paper presents a novel robustness-based formulation for control for discrete time minimum and non-minimum phase systems using the Virtual Reference Feedback Tuning (VRFT) framework. The proposed idea is to design robust lower and higher order Proportional Integral and Derivative (PID) controller using Maximum Sensitivity (Ms) based closed-loop reference model by minimization of the VRFT objective function (JVR) The efficacy of the approach is verified on various systems in simulation and also demonstrated on the Temperature Control Process and Level Control Process experimental setups. The proposed VRFT approach provides improved performance and robustness for set point tracking and disturbance rejection. Also, the proposed approach ensures stability and specific robustness of the closed-loop system. Further, the fragility of the controller is investigated for perturbations in the controller parameters.

A New VRFT Approach for IMC-PID Feedback-Feedforward Controller Design based on Robustness

This paper presents, design of feedback-feedforward controllers based on Internal Model Controller (IMC) strategy using data based Virtual Reference Feedback Tuning (VRFT) approach. For minimum phase discrete systems, some VRFT approaches have been proposed to design IMC based PID controllers in the literature. However, the approaches determine whether the system is robust or not. In this paper, a new VRFT approach is investigated. The IMC based PID controllers are designed by scaling controller tuning parameters for specified robustness in addition of non-measurable disturbance signal. Different control structures, based on feedback and feedback plus feedforward and IMC filter, are evaluated both concerning regulatory and servo control. The simulation results show that, the proposed method enhance the performance for feedforward structures with filter based on same degree of robustness in terms of Integral Square Error (ISE), Total Variance (TV), VRFT Objective Function (JVR).

Deterministic continuous-time Virtual Reference Feedback Tuning (VRFT) with application to PID design

In this paper, we introduce a data-driven control design method that does not rely on a model of the plant. The method is inspired by the Virtual Reference Feedback Tuning approach for data-driven controller tuning, but it is here entirely developed in a deterministic, continuous-time setting. A PID autotuner is then developed out of the proposed approach and its effectiveness is tested on an experimental brake-by-wire facility. The final performance is shown to outperform that of a benchmark model-based design method.

Data-Driven Approach for Current Control in DC-DC Boost Converters

Abstract This work addresses the control of the inductor current in DC-DC boost converter using a data-driven approach. Model-based approaches tend to be either conservative or complex tasks when considering intrinsic non-linearities. In that sense, data-driven solutions to the control design of such processes come in handy as they do not need a plant model and, given a sufficiently rich experiment, all information needed from the system at a given operation point is obtained from data. Even for a specific data-driven method - in this work, the Virtual Reference Feedback Tuning (VRFT) - there are different ways to implement it, which allow tuning of different controller structures. In this work, we compare and apply these approaches on the control of the inductor current in a DC-DC boost converter directly on an actual plant and show that, for this kind of plant, the VRFT approach in which the inverse of the controller is identified along with the “derivative” pole outperforms the standard one.

Robust direct data‐driven controller tuning with an application to vehicle stability control

SummaryIn direct data‐driven controller tuning, a mathematical model of the plant is not needed, as the control law is directly derived from experimental data. Because the most widely used data‐driven techniques are based on the assumption that the underlying dynamics – albeit unknow – is linear, the performance of the resulting controller may not be acceptable with systems whose operating region vary along the time. In this paper, we discuss how to robustify linear data‐driven design by exploiting the features of scenario optimization. More specifically, we carry out a modified version of the well known virtual reference feedback tuning approach where probabilistic performance guarantees are given also when the current operating condition is different from the one observed in the controller identification experiment. We validate the proposed approach on a vehicle stability control problem, via a thorough simulation campaign on a multibody simulator. The experimental results show the effectiveness of the proposed approach in a complex real‐world setting. Copyright © 2017 John Wiley & Sons, Ltd.

Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control

Virtual Reference Feedback Tuning (VRFT) is a data-driven one-shot control method which is very attractive for engineering applications. However, it cannot design controllers with the optimal control performance based on the standard VRFT approach as performance indices are not explicitly represented in its objective function. To deal with this problem, this paper presents a novel intelligent VRFT (IVRFT) based on adaptive binary ant system harmony search (ABASHS) where the reference model of VRFT, which potentially determines the control performance, is coordinately optimized with the controller by ABASHS to achieve the best control performance. Finally, the proposed ABASHS-based intelligent virtual reference feedback tuning (ABASHS-IVRFT) method is applied to the temperature control of the heat treatment electric furnace. The simulation results demonstrate that ABASHS-IVRFT is valid and can implement the optimal non-overshoot control easily and efficiently. Considering the characteristics such as ease of implementation and no need of the model information of controlled objects, ABASHS-IVRFT is a promising approach for engineering applications.

Virtual Reference Feedback Tuning for industrial PID controllers

Abstract In this paper, we propose a data-based auto-tuning method for industrial PID controllers, which does not rely on a model of the plant. The method is inspired by the Virtual Reference Feedback Tuning approach for data-based controller tuning, but it is taylored to the framework of PID controller design. The method is entirely developed in a deterministic, continuous time setting, where the assumption of stationarity is not needed. The effectiveness of the proposed approach is tested on a benchmark example that has been recently proposed for the evaluation of PID controllers.

A Data-Driven Control Design Approach for Freeway Traffic Ramp Metering with Virtual Reference Feedback Tuning

ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT) is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEA's parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.

Digital Control of a Waste Water Treatment Plant

The Activated Sludge Process (ASP) is arguably the most popular bioprocess utilized in the treatment of polluted water. The ASP is described by means of a nonlinear model and results on a Two-Input Two-Output multivariable system. In this paper a discrete time digital control is proposed where the design of a decentralized controller is faced. Local controllers are given the form of a Two-Degree-of-Freedom PI controller tuned using the data-driven Virtual-Reference Feedback tuning approach.

Integrating virtual reference feedback tuning into a unified closed-loop identification framework

This paper compares particular instances of control-relevant identification, closed-loop identification, and controller identification (either in an actual loop or in a virtual reference feedback tuning approach, VRFT) problems. Significant similarities appear between them which allow, in particular, alternative formulations of the VRFT methodology in a differentiable setting.