Application of Multivariable Virtual Reference Feedback Tuning with Anti-Windup to the Benchmark PID 2018

This paper presents an application of the Virtual Reference Feedback Tuning (VRFT) to control of the single-phase inverters. The VRFT is a data-driven method able to tuning controllers from data, this method only requires one data batch (input/output) to estimate parameters of a controller, and does not require the plant model. Under the VRFT framework, this work explores different controller structures and input designs, in order to find that controllers are able to deal with disturbances and nonlinear loads for a single phase inverter. Simulated results show that is possible controller design for a single phase inverter from data, avoiding the plant modeling. The main purpose of this research is to comparate VRFT with a standard controller model-driven.

This paper is concerned with data-driven methods for virtual reference controller design of high-order nonlinear systems via neural network. Virtual reference feedback tuning (VRFT) is a one-shot direct data-based method to design controller of linear or nonlinear systems. In this paper, we recall the model reference control problem of high-order nonlinear systems and design a new objective function of VRFT. In ideal conditions, the two problems are demonstrated to have the same solution. For the first time, we prove that the value of the optimization problem for model reference control is bounded by that of the objective function of VRFT. A three-layer neural network is employed as a general approximator of the designed controller and two simulations are given to verify the validity of our method.

Design of a Data-Driven PID Controller using Operating Data

Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems

The current control system of an industrial box furnace usually uses a proportional-integral-derivative (PID) controller. Typically, this control system requires on-site tuning and specific heuristic knowledge, such that the furnace can have acceptable performance but not optimal. However, by using a proper model in the operating range of the furnace from the designing phase, it can be designed a more efficient control system capable to reach better performance in this industrial process. In this sense, the main objective of this paper is the design and validation of an advanced control strategy in the early design stage of an industrial box furnace. The goals are to decrease the controller tuning time during the furnace commissioning process, improve the furnace performance with respect to classical industrial controllers, and decrease the system energy expenses. Thus, a comparative analysis in a simulated environment has been carried out between the performance of a PID controller by its typical industrial use, a model predictive control (MPC) due to the optimal results in similar industrial processes, and a virtual reference feedback tuning (VRFT) control by its feasibility of implementation. Results show that the advanced MPC and VRFT controllers are more efficient in the used fuel/gas than the PID. MPC shows the best balance between performance and actuation, it improves the control performance up to 87.2% with respect to the VRFT controller.

We propose herein a data-driven dead-zone (DZ) compensation strategy using a model-free Virtual Reference Feedback Tuning (VRFT) approach. The VRFT tuning scheme is accommodated for two controller structures: the first one which explicitly includes a model of the DZ inverse to be identified and the second one which uses a Neural Network (NN) to model the controller to be identified. The main question to be answered here is whether if the inclusion of an explicit model of a static nonlinearity (DZ in this case) can be avoided while preserving the control system performance. Thorough investigation case studies are carried out both in simulation and experiment on a laboratory 3D-crane system as a typical servo system control application.

Data-driven controller synthesis methods use input/output information to find the coefficients of a proposed control architecture. Virtual Reference Feedback Tuning (VRFT) is one of the most popular frameworks due to its simplicity and one-shoot synthesis style based on open-loop system response for classic regulators such as PI or PID. This paper presents a recursive VRFT framework to extend VRFT into high-order controllers with more complex structures. The framework first defines a reference model and controller structure, then uses the open-loop data to compute the virtual reference and error signals, and, finally, uses these to find the controller parameters via an optimization algorithm. Likewise, the recursive VRFT controller performance is improved by adding a model-based feedforward loop to improve reference signal tracking. The recursive method is tested to design a temperature uniformity control system. The obtained results show that the recursive VRFT with a feedforward improves the system response while allowing more complex controller synthesis.

The simplified modeling of a complex system allied with a low-order controller structure can lead to poor closed-loop performance and robustness. A feasible solution is to avoid the necessity of a model by using data for the controller design. The Virtual Reference Feedback Tuning (VRFT) is a data-driven design method that only requires a single batch of data and solves a reference tracking problem, although with no guarantee of robustness. In this work, the inclusion of an H∞ robustness constraint to the VRFT cost function is addressed. The estimation of the H∞ norm of the sensitivity transfer function is extended to maintain the one-shot characteristic of the VRFT. Swarm intelligence algorithms are used to solve the non-convex cost function. The proposed method is applied in two real-world inspired problems with four different swarm intelligence algorithms, which are compared with each other through a Monte Carlo experiment of 50 executions. The obtained results are satisfactory, achieving the desired robustness criteria.

Closed-loop control of FES supported standing up and sitting down using virtual reference feedback tuning: A simulation study

Data-driven MIMO model-free reference tracking control with nonlinear state-feedback and fractional order controllers

A McKibben-type artificial muscle has several advantages for practical applications, however, it is challenging to control it with high-precision due to its strong hysteresis characteristic caused by friction. For this reason, in this paper, a VRFT-MFC that combines Model-Free Control (MFC) and Virtual Reference Feedback Tuning (VRFT) was proposed, which is based on MFC structure, and VRFT optimizes the parameters of MFC with a pair of input-output data to satisfy a stability condition for the entire system. However, this method includes weights design that strongly affects control performance, and their tunings are time-consuming routines. To solve these problems, we propose a design policy for the weights. It is confirmed that the effectiveness and optimality of the proposed policy results for water-hydraulic McKibben-type artificial muscles in experiments.

This paper investigates a model-free tuning method of a fractional-order proportional–integral (FOPI) controller and its application for the speed regulation of a permanent magnet synchronous motor (PMSM). Firstly, the presented practical FOPI tuning method formulates the FOPI controller parameter identification problem via virtual reference feedback tuning (VRFT). Under the lack of accurate models, the proposed model-free method depends only on the measured input–output data of the closed-loop PMSM servo system. Secondly, Bode’s ideal transfer function is incorporated into the VRFT with consideration of the systematic fractional dynamics. Thus, the properties of the resulting system may be approximated to the desired fractional-order reference model. Thirdly, the proposed method fully considers optimal performance constraints on the stability requirements, sensitivity criteria, frequency-domain and time-domain characteristics. Then, the comprehensive optimization problem is derived and solved. Using suitably tuned parameters, the robustness and disturbance rejection ability of the VRFT-based FOPI control system are enhanced to achieve optimal performance. The convergence of the proposed method is proved by theoretical analysis. Finally, experimental results are presented to illustrate the effectiveness of the proposed model-free FOPI control method for the PMSM servo system.

Data-driven two-dimensional integrated control for nonlinear batch processes

This paper provides an application of the virtual reference feedback tuning (VRFT) method to a process control. The VRFT method is a direct method which determines parameters of a feedback controller using a set of input and output data of a plant. Originally, the VRFT method is presented for a design of a single-input single-output control system. In this paper, the VRFT method is applied for a design of a multi-input multi-output system. Experimental test is carried out and test results show availability of the VRFT method.

SUMMARYThe paper gives a controller tuning method using a virtual reference feedback tuning (VRFT) approach in frequency domain. While the original VRFT uses a data‐driven performance index based on input/output data directly, the proposed one derives a frequency domain performance index based on the Fourier transformation of one‐shot experimental input‐output data. The frequency domain performance index enables the optimal prefilter to be straightforwardly designed without using the spectral density of the collected input signal. Hence, the proposed one outperforms the original one when the input and output data are collected from the closed loop step response. The effectiveness of the proposed method is illustrated through a numerical example.