Earticle

이 논문은 시스템 식별 기법을 이용하여 외팔보 진동제어 시스템의 수학적 모델을 구한 후 PID 제어기를 장착하여 효과적인 진동제어 결과를 제시하였다. 압전세라믹(piezo-ceramic)은 작동기로서 가장 기본적인 유연 구 조물의 형태인 외팔보의 진동을 억제하는데 사용되었으며 레이저 센서가 보의 굽힘 변위를 측정하는데 사용되었 다. 압전 세라믹의 입력 전압으로부터 레이저 감지기의 출력 전압까지의 전달함수를 계단입력에 대한 응답 실험데 이터에 시스템 식별 기법을 적용하여 구하였다. PID제어기는 산업현장에서 널리 사용되고 있으나, 적절한 이득 값 을 결정하는 것은 간단하지 않아, 본 연구에서는 Ziegler-Nichols 기법을 통해 PID 제어기를 튜닝하여 압전 세라 믹을 작동기로 사용하는 외팔보 시스템의 진동 제어에 적용하였고 시뮬레이션과 실험적 결과를 통하여 시스템식 별 기법을 통해 구한 수학적 모델의 유용성과 설계된 제어기의 효과적인 진동 제어 성능을 확인하였다.

In this paper first we obtained a mathematical model for a cantilever beam vibration control system with a piezoceramic actuator using system identification techniques and then implemented a PID controller for active vibration control. A piezoceramic is used as an actuator to control the vibration of a cantilever beam which has been used in many real structures, and a laser sensor is used to measure tip displacement of the cantilever beam in transverse motion. A transfer function from piezoceramic’s input voltage to laser sensor’s output voltage is obtained using system identification techniques utilizing experimental data from step response of the open-loop cantilever beam control system. The PID controller is tuned by Ziegler-Nichols technique and implemented to the beam control system. The result exhibits good performance in terms of vibration reduction, which validates the identification of the system and tuning of the PID controller.

본 논문에서는 쿼드콥터형 드론 시스템을 구현하고, PID 제어기에서 안정화 시간을 최소화할 수 있는 이득 계수를 구하기 위한 경험적 방법을 제안하였다. 드론 자세 안정화 제어 시스템은 가속도 센서와 자이로 센서의 자세 정보를 마호니 필터를 통해 보정한 후 PID 제어기를 통해 4개의 모터를 구동한다. 제안된 방법은 기존의 지글러-니콜스 방법을 통해 1차적으로 이득 계수를 구한 후에 각각의 이득 계수를 경험적 방법으로 다시 최적화하는 단계를 거쳐서 결정한다. 최종적으로 구해진 이득 계수를 구현된 시스템에 적용하여 롤 방향으로 20도 회전하는 실험을 수행한 결과 기존의 지글러-니콜스 방법에 비해 최대 오버슈트는 44.3도에서 29.8도로 감소하면서도 안정화시간이 2.6초에서 1.7초 로 개선됨을 확인할 수 있었다. 따라서 제안된 방식은 최적의 이득 계수를 구하기 위한 시행착오를 줄이면서도 드론과 같이 모터 잡음이 심한 환경에서도 잘 동작함을 실험을 통해 증명하였다.

This paper implemented quadcopter-type drone system and proposed the heuristic method for obtaining the optimum gain coefficients in order to minimize the settling time. Control system for quadcopter posture stabilization reads the posture data from accelerator and gyro sensor, revises the original posture data using Mahony filter, and drives 4 DC motors using PID controller. The first step of the proposed method is to obtain the gain coefficients using the Ziegler-Nichols method, and then determine the optimum gain coefficients using the heuristic method at the next 3 steps. The experimental result shows that the maximum overshoot decreases from 44.3 to 29.8 degrees and the settling time decreases from 2.6 to 1.7 seconds compared to the Ziegler-Nichols method. Therefore, we proved that the proposed method works well in quadcopter flight system with high motor noise while reducing trial and error to obtain the optimal PID gain coefficients.

Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

본 논문에서는 절대 안정도를 보장하는 최적의 제어기 설계에 대해 제안하였다. 논문의 적용 순서는 지연 시간의 포함여부를 판단하고, 지연시간이 포함되었을 경우 Pade 근사법을 통해서 지연시간을 근사화 한다. 그 다음 공정모델과 제어기 전달함수에 대한 개루프 전달함수를 구하며, Routh-Hurwitz 판별법에 의해서 절대 안정 도 구간을 계산한다. 마지막 단계에서는 앞 단계에서 구한 구간을 활용하여 유전자 알고리즘으로 최적의 PID 제어 파라미터 값을 구한다. 그 결과 제안 된 방법은 안정성이 보장되며, 최적의 제어기를 설계하여 기존의 방법보다 성능 지표에서 우월함을 확인하였다. 향후 지연시간에 대한 보상방법이 연구된다면 더욱 좋은 성능지표를 얻을 것으로 판단된다.

In this paper, an optimal controller design that guarantees absolute stability is proposed. The order of application of the thesis determines whether the delay time is included, and if the delay time is included, the delay time is approximated through the Pade approximation method. Then, the open loop transfer function for the process model and the controller transfer function is obtained, and the absolute stability interval is calculated by the Routh-Hurwitz discrimination method. In the last step, the optimal Proportional and Integral and Derivative(PID) control parameter value is calculated using a genetic algorithm using the interval obtained in the previous step. As a result, it was confirmed that the proposed method guarantees stability and is superior to the existing method in performance index by designing an optimal controller. If we study the compensation method for the delay time in the future, it is judged that better performance indicators will be obtained.

This paper proposes a fusion controller combing an anti-windup PID controller and BELBIC (Brain Emotional Learning Based Intelligent Controller) for controlling the position and vibration of a robot system having a single flexible manipulator. A finite element model of the flexible manipulator is developed. The reliability of it is verified by comparing the natural frequencies computed using the finite-element method with the experimentally measured ones. An MSC.ADAMS computational model of the robot system is interfaced with the proposed controller in MATLAB/Simulink, for carrying out a simulation. The simulation is performed with various references inputs and endpoint masses. The effectiveness and robustness of the proposed controller for control of the position and vibration of the flexible manipulator is shown through the simulation.

PID auto-tuning controller was designed via fuzzy logic. Typical values such as error and error derivative feedbackwere changed as heuristic expressions, and they determine PID gain through fuzzy logic and defuzzification process. Fuzzy procedure and PID controller design were considered separately, and they are combined and analyzed. Obtained auto-tuning PID controller by Fuzzy Logic showed the ability for less than 3rd order plant control.

본 논문은 선형 PID 제어기의 설계 방법에 대해서 설명하였고, 향후 설계 방법에 대해서 제안 하였다. 첫 번 째 PID 설계 방법으로는 위상여유와 이득여유를 보장하는 방법이다. 이 방법은 주파수 영역에서 설계하는 것으로 안정 도를 보장한다. 두 번째 방법은 내부 모델 제어 방법이다. 이 방법은 제어 모델에 대한 내부 모델을 동정 후 내부 모델 의 파라미터를 이용하여 PID 제어기를 설계하는 것이다. 따라서 이 방법은 외란에 강한 특성을 갖고 있다. 마지막으로 제안하는 것은 Cascade-smith-Predictor 제어기 이다. 이방 법의 Cascade 제어기와 smith-Predictor의 구조를 결합한 것으로 강인제어와 최적제어 두 가지 장점을 갖는 제어기 구조이다. 이 방법은 최적 제어기 설계 방법으로 성능 평가 지수를 얻을 수 있을 것이다. 이와 같은 PID 제어기 설계 방법은 비선형 방법의 기초가 되며, 지속적인 연구가 수행되고 있다.

This paper describes the design method of the linear PID controller and proposed the design method in the future. The first PID design method is to ensure phase margin and gain margin. This method guarantees stability by designing in the frequency domain. The second method is an internal model control method. This method is to design the PID controller using the parameters of the internal model after identifying the internal model for the control model. Therefore, this method has a strong disturbance characteristic. Finally, a proposed Cascade and smith-Predictor controller. The combination of the cascade controller and the smith-predator of this method is a controller structure that has two advantages: robust control and optimal control. This method can obtain the performance evaluation index as the optimal controller design method. This PID controller design method becomes the basis of the nonlinear method and is being continuously studied.

Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

비행중인 드론에는 매우 다양한 주파수 성분의 진동이 발생되고, 이러한 진동 환경에서 드론에 장착된 카메라로부터 깨끗하고 안정된 영상을 획득하기 위해서는 짐발 시스템의 안정화 설계가 필요하다. 짐발 시스템은 카메라 모듈을 지지 하는 구조와 외부로 부터의 진동을 차단하면서 정확한 각도를 추종하는 안정화기로 구성된다. 본 논문에서는 짐발시스 템의 한 축에 대한 동역학 모델을 세우고 이에 대한 고전적인 PID제어기를 적용하여 본다. 또한 시스템에 대한 동적 모 델 없이 Intelligent-PID 제어기를 설계하고, 두 제어기의 성능을 MATLAB/Simulink을 이용한 시뮬레이션으로 비교하여 본다. 이들을 통하여, Intelligent-PID 제어기는 동역학 모델을 거의 필요로 하지 않고도 설계가 가능하고, 모델의 특성이 변하여도 제어기의 파라미터를 재조정할 필요가 없이 진동을 차단하고 각도를 추종 할 수 있는 제어강인성을 보인다.

A flying drone generates vibrations in a great variety of frequencies, and it requires a gimbal system stabilization loop design in order to obtain clean and accurate image from the camera attached to the drone under this environment. The gimbal system for drone comprises the structure that supports the camera module and the stabilization loop which follows the precise angle while blocking the vibration from outside. This study developed a dynamic model for one axis for the stabilization loop design of a gimbal system for drones and applied classical PID controller and intelligent PID controller. The Stabilization loop design was developed by using MATLAB/Simulink and compared the performance of each controller through simulation. Especially, the intelligent PID controller can be designed almost without the dynamic model and it demonstrates that the angle can be followed without readjusting the parameters of the controller even when the characteristics of the model changes.

Recently, a study on reducing the weight of the robot arm, which enables a high-speed operation and enables reducing the energy consumption has been actively carried out. A lightweight robot arm is hard to control because it behaves like a flexible body rather than a rigid body. This paper proposes a controller which combines a PID controller and a fuzzy logic controller for control the position and vibration of the flexible robot arm. In order to show the effectiveness of the proposed controller, MSC.ADAMS computational model which incorporates the finite element flexible robot arm model is developed, and is used for performing simulations. Simulations are carried out with two reference inputs, and three end masses. Simulation results show that the proposed controller controls the position and vibration of the flexible robot arm adaptively without being affected by the reference input and the end mass.

산업자동화의 고정밀도에 따라 강인한 제어가 요구되고 있다. 하지만 PID 제어기를 갖는 전동기 시스템이 부 하 외란을 받게 되면 제어시스템의 강인 제어는 어렵게 된다. 이에 대해 보완적인 한 방법으로 본 논문에서는 전동기 제어 시스템을 위한 PID-전문가 복합형 제어 기법을 제시하였다. 만약 PID 제어 시스템이 안정한 상태에 있다면 전문 가 제어기는 사용되지 않는다. 그러나 오차가 일단 발생하게 되면, 전문가 제어기는 오차를 구속 영역 내로 들어가도록 제어한다. 이에 따라 외란의 영향은 현저히 감소하게 된다. PID-전문가 복합형 제어기를 이용한 직류 서보 전동기의 강인성을 시뮬레이션에 의해 확인하였다.

Robust control for DC motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. In this paper, PID-Expert hybrid control method for motor control system as a compensation method solving this problem is presented. If PID control system is stable, the Expert controller is idle. if the error hits the boundary of the constraint, the Expert controller begins operation to force the error back to the constraint set. The disturbance effect decrease remarkably, robust speed control of DC motor using PID-Expert Hybrid controller is demonstrated by the simulation.

Autonomous driving of mobile robot is important in various working environments. For its autonomous driving, mobile robot utilized ultrasonic sensor to detect obstacles and avoid them. Also, there have been researches on supplementing function of avoiding crash by using control methods based on adoption of intellectual system. This research has designed type-2 fuzzy self-tuning PID controller which can enhance autonomous driving function of mobile robot under fixed obstacle environment. The fuzzy controller has utilized type-2 with multiple values and the fuzzy self-tuning PID controller adjusting gain value of PID controller is applied. 25 rules have been reduced to 9 rules to improve function of hardware. Type-2 fuzzy self-tuning PID controller is used to compare function of traditional PID controller and Type-1 fuzzy self-tuning PID controller. In terms of function of Type-2 fuzzy controller with reduced number of rules, it was confirmed through improvement in function of autonomous driving of mobile robot using MATLAB/Simulink.

This paper presents a development of a self-tuning fuzzy PID controller to overcome the appearance of nonlinearities and uncertainties in the system. The self-tuning fuzzy PID controller is the combination of a classical PID and fuzzy controller. The controller is designed based on the expert knowledge of the system. Fuzzy logic is used to tune each parameter of PID controller. Appropriate fuzzy rules are designed to tune the parameter Kp, Ki and Kd of the PID controller, the performance of the buck converter has improved significantly compare to conventional PID controller.

In allusion to the insufficient of the traditional parameters optimization of the traditional fuzzy neural network PID controller, the parallel search characteristics of the ant colony algorithm in the whole parameter space is used. A parameters optimization method of the PID controller based combining the ant colony algorithm and fuzzy theory and neural network is proposed in this paper. The method used the ant colony algorithm to comprehensively optimize the parameters and structure of fuzzy neural network, which to be used to train and determine the parameters of the PID controller in order to get the fuzzy neural network PID controller. This method is used in the practical application of nonlinear coupled system, the experimental results show that the optimized fuzzy neural network PID controller takes the faster approximation control objectives, the shorter response time, the smaller overshoot and higher control accuracy. Consequently, the research has the theoretical significance and practical application value.

An efficient controller based on joint between original proportional-integral-derivative (PID) controller and fast genetic algorithm (FGA) is proposed to enhance the transient response of high order synchronous machine. PID controller has several advantages in practical design of synchronous machine compared with another controller。The transient response parameters with PID controller of synchronous machine are reasonable, but they susceptible to the local minima problem and not adequate. Therefore, FGA is used to overcome this problem and to enhance the transient response. Performance of the proposed controller is tested through a high order synchronous machine model and then compared with the original PID controller. In this paper the weak coupling relationship between the automatic voltage controller (AVR) and automatic generation control (AGC) in the synchronous machine has been proved to simplify the overall model. The results demonstrate that, the proposed controller is an efficiency and better than a conventional PID controller.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

In this paper, a novel methodology, based on fuzzy logic, for the tuning of proportional-integral-derivative (PID) controllers is presented. The purpose of this project tries to explore the potential of using soft computing methodology in controllers and their advantages over conventional methods. In control system there are a number of general systems and methods which are encountered in all areas of industry and technology. There are many ways to control any system, in which fuzzy is often the very best way. The only reason is faster and cheaper. For this paper, the set-point tuning was controlled by using three rules of membership function which then extended to five rules, seven rules and nine rules for verification purpose and further improvement of the system. There are various systems for the designing of PID controller and it is used to control the different parameters like settling time, rise time, overshoot, peak gain and phase margin, stability etc of the plant. Hence both the overshoot and the rise time in set-point following can be reduced. The conventional PID controller is not very efficient due to the presence of non linearity in the system of the plant and also it has a quite high overshoot and settling time. The main focus of this project is to apply soft computing technique that is fuzzy logic to design and tuning of PID controller to get better dynamic and static performance at the output. This project also discusses the benefits the soft computing methods.

This paper emphasises on the control of a magnetic ball levitation system using an enhanced proportional integral derivative (PID) controller. Though PID controllers produce a controlled output for stable and unstable systems, the performance of the system in terms of overshoot, settling time, Integral Square Error (ISE) and Integral Absolute Error (IAE) are poor. To solve this problem, an enhanced PID controller is designed based on the system open loop transfer function. A compensator in cascade with the PID controller is designed based on bode plot response of the system. The efficacy of the proposed controller is demonstrated via simulation study of a magnetic ball levitation system. The results are compared with the classical PID controller. The proposed controller eliminates the overshoot and reduces ISE and IAE, thus provides smoother system responses.