Design of a Data-Driven PID Controller using Operating Data

In modern industry, data has become an indispensible resource for the whole process of production and business. For the optimization of the control performance, the data-driven PID controller is introduced in this paper. Based on the data-driven control law, the parameters of the PID controller will be renewed automatically by using the real and historical input/output data of the plant. And then, the control action can be update in real time. From the perspective of engineering applications, the data-driven PID controller is researched as the outer loop controller in the superheated steam temperature cascade control system of fossil fuel power generation units. The simulation results show that the data-driven PID controller is suitable for the superheated steam temperature control, and its performance is better than that of the neural network PID controller.

This paper proposes a new offline learning algorithm for a data-driven proportional-integral-derivative (DD-PID) controller based on an extended fictitious reference iterative tuning (E-FRIT). PID controllers have been used in many process systems. However, it is difficult to maintain a good control performance using PID controllers with fixed control parameters because of the nonlinearity of the systems. The DD-PID controller has been proposed as an effective control system for nonlinear systems. This controller can tune its PID parameters adaptively at each equilibrium point of the system output. In the conventional DD-PID controller, the PID parameters are learned so as to minimize a criterion of the FRIT method. However, the FRIT method is based on minimization of the error in system output, and therefore, the criterion is insufficient for systems, such as chemical process systems, for which the stability of a closed loop system is essential. In order to solve this problem, the E-FRIT method has been proposed; a penalty for input variation is incorporated in the criterion for this method. In the present study, an offline learning rule of PID parameters was derived based on the E-FRIT criterion. According to the rule, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method was evaluated by simulations of a polystyrene reactor system. The simulation confirmed that the proposed DD-PID controller yields better control result than the conventional learning method.

This paper proposes a method for quadrotor tuning focusing on attitude control (roll, pitch, and yaw channel). The controller is based upon a double-loop PID structure that is widely used in many flight controller boards. However, it is not easy to tune the parameters of a PID controller such that it satisfies the desired transient response, especially using manual tuning. Thus, this paper proposes a method for quadrotor tuning with a double-loop control structure by using fictitious reference iterative tuning (FRIT) for attitude control. The proposed method is applied to tune the gains of a double-loop PID controller by using FRIT so that the attitude response of a quadrotor matches with the desired response for each channel by using only one-shot experimental input-output data. Implementation results are provided to show the effectiveness of the proposed method.

DESIGN AND EXPERIMENTAL EVALUATION OF A DATA-DRIVEN PID CONTROLLER

In this paper, we propose an off-line tuning method of the vibration suppression controller gains for 2-inertia system using Fictitious Reference Iterative Tuning (FRIT). FRIT is one of data-driven design methods using only one-shot experimental input-output data without model parameters of the controlled object. We use the modified-IPD speed controller which consists of the 2-degree of freedom PID control, considering response speed, and in addition, we use the first lag element in order to increase degree of freedom of the control design. Then, we consider the current loop for the high speed torque control. In this paper, a PI controller is used for the current control. Here, in this paper, we use the particle swarm optimization as the optimization searching method, and the performance index is the IAE. So, we confirmed the effectiveness of the proposed method which used only one-shot experiment result by experimental setup.

PID control has been applied in many real systems. The control performance strongly depends on PID parameters. Although, some schemes for tuning PID parameters have been proposed, these schemes require system parameters which are estimated by system identification in order to calculate PID parameters. Moreover, most process systems are multivaliable systems which have interference of each respective inputs. On the other hand, data-oriented controller design schemes represented by VRFT or FRIT have received much attention in the last few years. These methods can calculate control parameters using closed loop data, and are expected to reduce computational costs. However, these methods have not been extended for MIMO systems. In this paper, a type of data-oriented multivariable PID controller using closed loop data is proposed. According to the proposed method, full matrices PID parameters which suppress mutual interference are calculated based on the implicit GMVC. Moreover, the control performance can be suitably adjusted by user-specified parameters. The effectiveness of the proposed method is numerically and experimentally evaluated.

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, PID controllers have been widely used for process systems. Therefore, A new design scheme of PID controllers based on a memory-based (MB) modeling is proposed for nonlinear systems. According to the MB modeling method, some local models are automatically generated based on input/output data pairs of the controlled object stored in the database. The proposed scheme generates PID parameters using stored input/output data in the database. This scheme can adjust the PID parameters in an on-line manner even if the system has nonlinear properties. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

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.

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

For the stability problem of the active magnetic levitation ball, the application of the PID controller on the system is determined. Firstly, the linearized mathematical model of the active magnetic levitation ball system is established. Then the PID parameters are adjusted by the trial method. The influence of three PID parameters on the system is introduced, and a PID controller is designed for a set of actual systems. The performance is simulated and experimentally studied. The research results show that the active electromagnetic suspension ball system based on PID controller has high anti-interference performance and stability.

This paper presents a new strategy for online robust control-based fractional order PID controller (FPID). The technique suggests an online parameters tuning of the FPID using fictitious reference iterative tuning (FRIT) approach. This approach enables us to track the desired optimal FPID parameters online using experimental data without any plant model identification. After having set the optimal non-integer orders α and β, the gains of the FPID are then estimated in a recursive manner using the so-called recursive least squares (RLSs) algorithm with forgetting factor, which can cope with variation of plant characteristics adaptively. The performances of this technique are compared with other results already existing in the literature through some typical examples; the results of the proposed approach show significant performance improvement.

This paper develops a robot trajectory tracking system based on least-squares gain PID control. Different from the offline adjustment method of PID controller, even if the environment (for example, the shape of the target blue line or the friction of the ground) is constantly changing, only a set of fixed PID parameters are used. LMS can estimate the turning angle online so that can adjust it in real time PID gain (its parameter). PID parameters change continuously and become dynamic. The dynamic parameters of the PID controller will control the robot to perform more precise line tracking actions, thereby simulating automatic driving.

SummaryProportional Integral Derivative (PID) controllers still represent the core control method for achieving output regulation of either linear or nonlinear systems in the majority of industrial applications. However, conventional PID control cannot guarantee specific state constraint requirements for the plant, when the system introduces uncertainties. In this paper, a novel nonlinear PID control that achieves output regulation and guarantees a desired state limitation below a given value for a wide class of nonlinear systems with constant uncertainties is proposed. Using nonlinear ultimate boundedness theory, it is shown that the proposed state‐limiting PID (sl‐PID) control maintains a given bound for the desired system states at all times, ie, even during transients, whereas an analytic method for selecting the controller gains is also presented to ensure closed‐loop system stability and convergence at the desired equilibrium. Two nonlinear engineering examples that include an electric motor and a dc/dc converter are investigated using the conventional PID and the proposed sl‐PID to validate the superiority of the proposed controller in achieving the desired output regulation with a given bounded state requirement.

This paper proposes a new method for designing a self-tuning PID controller. A PID controller is designed on the basis of generalised predictive control with a terminal matching condition (γGPC) to automatically tune its PID parameters. The proposed PID controller has a time-varying proportional gain to precisely achieve γGPC performance. Furthermore, a PID controller is designed on the basis of γGPC extended by using coprime factorisation to obtain stable proportional gain. Hence, γGPC can be well approximated by the proposed PID controller. A numerical example demonstrates its effectiveness.

The PID controller and real-time monitoring system for a low-temperature furnace was developed. The system has two part, the PID controller, and the real-time monitoring part. An Arduino mega2560 microcontroller board was used for measuring and control the furnace temperature. A type-K thermocouple and a MAX31850 IC was applied for a furnace temperature measurement. The microcontroller board and a MAX31850 were connected via the One-wire bus for convert the temperature values and sent to a personal computer. The PID parameters can be varied by a user in the program, which developed by LabVIEW Software on a computer. The laboratory made furnace was established for testing the controller and monitoring system. The results have shown that the temperature with the range of 25-500 degree Celsius can be controlled. By the trial and error method with the PID parameters, kp was 250, Ti was 0.05 and Td was 0.20, the target temperature can be controlled with the maximum error of 1 degree Celsius.