E-FRIT法に基づくデータ駆動型PID制御系の学習法

Design of a Data-Driven PID Controller using Operating Data

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.

This paper considers USV heading controller using Adaptive PID and LQG controller. PID is a conventional controller and the most industry controller. However, PID parameters are selected by trial and error. That problem proposed Adaptive PID how to tune PID parameters. In this paper, PID parameters are estimated by RLS method. The second controller for comparison is LQG. LQG controller is robust controller. A gain regulator estimation method, Kalman Filter, is used as LQG controller. The computational results show the LQG controller has better performance than Adaptive PID.

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.

Different industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

Through LFC tie-line interchange power of the area within prescribed limit and scheduled frequency of the system can maintain constant. Generally, Integral (conventional) Controllers are used in Load Frequency Controllers but they are quite slow. In this paper, we have used the PID controller for two-area power system in place of conventional controller and by using various adaption techniques we are tuning controllers. In order to verify the performance of PID and conventional controller, comparative results of both controllers are also presented. Overshoot and settling time are better with PID controller. The result demonstrates the effectiveness of PID controller over conventional controller. Different techniques like Ziegler Nichols and Nonlinear Control Design technique are used for comparing the responses of conventional controllers with PID controllers.

Inertial platform stabilization loop is required with good quickness, high steady precision, strong anti-jamming capability, etc. at the same time, and the traditional PID control is difficult to meet index requirements of the good control performance. Stabilization loop of inertial navigation system platform is treated as a study object, and the mathematical model is built with dynamic loop according to the position of PID controller. Based on the self-adaptive online genetic algorithm tuning, PID control strategy is proposed, and the PID parameters are optimized, and loop model is simulated with MATLAB software. The result of simulation shows that since PID controller is adopted after the genetic algorithm optimization, system control precision, stability and response speed have been improved, enhancing the robustness.

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.

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the genetic algorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part and the on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step response as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p, T*i, and T*i are obtained, which are used as the inital values for the on-line tuning of PID parameters. In a on-line part, based on K*p, T*i, and T*d and according to the current system error e and its time derivative, a dedicated program is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mechanism to ensure that the system response has optimal dynamic and steady-state performance. The controller has been used to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Despite the widespread use of PID controllers in the drone industry, they often suffer from incorrect tuning, especially in unexpected environments. The complexity of modern businesses motivates the development of unique approaches to improve PID controllers. This paper uses Deep Q-Network (DQN) to enhance the PID controller and overcome uncertainties in control systems. PID parameters are tuned using a dynamic neural network with a unique structural technique to optimize PID parameters, reducing the computing overhead of deep neural networks. A simulated environment in Ubuntu OS is used to autotune drone PID parameters and demonstrate the auto-tuning.

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In this paper, a new design scheme of performance-driven PID controllers whose PID parameters are adjusted based on a control performance criterion. Although a majority of studies have been focused on the derivation of the CPM index, the control parameter tuning method based on the CPM has been hardly studied. Conventional self-tuning controllers are tuned based on the variance of control errors and/or modeling errors. Few adaptive schemes use performance indice as tuning signals, which should be the main driving force in maintaining optimal operation, This paper develops a strategy for the tuning of an adaptive PID controller that is an approximation of a generalized minimum variance controller. The main driving signal for adaptive tuning is the degradation of the controller performance criterion. The effectiveness of the proposed method is numerically evaluated on two simulation examples.

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.

This paper concerns design and implementation of PID and fractional-order PID (FOPID) controllers to control position of an electromagnetically suspended ferromagnetic ball in a magnetic levitation (Maglev) system in real time. The Maglev system, manufactured by Feedback Instruments (Model No 33-210) is used as a platform to test the performance of proposed controllers. Parameters of PID and FOPID controllers are tuned by firefly algorithm (FA). FA is a metaheuristic algorithm based on movement of fireflies toward more attractive and brighter ones. PID and FOPID controllers are implemented in MATLAB and SIMULINK environment inside PC using fractional-order modeling and control toolbox. Controller in the SIMULINK environment inside PC is connected to the Maglev system through Advantech card. Effectiveness of proposed controllers is tested by checking the ability of the suspended ball to track a reference signal. Step change, sine wave, and square wave are used as reference signals. Real-time results have revealed satisfactory transient and steady-state responses over the contemporary existing controllers. FOPID controller showed better results compared to PID.

Pneumatic Muscle Actuator (PMA) has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.