• 반도체디스플레이기술학회지
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• 제16권4호
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• pp.46-51
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• 2017

Recent controller design techniques often discretize the target system and implement a discrete controller that is digitized to match the target system. When constructing such a discrete system, it is necessary to first determine the sampling time. The smaller the sampling time is, the more advantageous it can be made similar to the original system, but the cost is a problem when realizing such a configuration as hardware. On the other hand, the longer the time, the more different the system is from the original system, and eventually the control becomes impossible. In this paper, we consider the above problem and propose a more logical approach to determine the sampling time in the discrete system and investigate the relation with the differential controller. We also apply this process to a nonlinear system called ARAGO disc and verify its validity through computer simulation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• 한국정보전자통신기술학회논문지
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• 제16권6호
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• pp.400-405
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• 2023

현재 도립진자는 많은 분야에서 연구 중이며 미사일, 로켓, 등의 자세 제어와 2족 보행 로봇 등에 응용되고 있다. 본 연구에서는 256펄스의 로터리 엔코더와 DC 모터를 이용한 회전형 도립진자(Rotary Inverted Pendulum)를 구성하여 회전형 역 진자의 수직 자세 제어를 연구하였다. 비선형 시스템의 경우 복잡한 알고리즘과 제어기가 필요하지만 고전적이며 비교적 간단한 PID(Proportional Integral Derivation)알고리즘을 이용한 제어 방법을 회전형 도립진자 시스템에 적용하였으며 간단하지만 원하는 성능을 높이는 방안을 연구하였다. 본 연구에서 사용된 회전형 도립진자 시스템은 비선형적이고 불안정한 시스템으로 선형화된 모델링에서 마이크로칩 사의 dsPIC30F4013 임베디드 프로세서를 이용한 PID 제어기를 설계 및 구현하였다. 보통 PID 제어기는 하나 혹은 두 가지 이상을 조합하여 설계하며 우수한 제어 성능에 비해 구조가 간단하며 제어 이득 조정이 다른 제어기들에 비해 비교적 쉽다는 장점이 있다. 본 연구에서는 시스템의 물리적 구조를 수학적 방법으로 분석하고 모델링을 통한 회전형 도립진자의 수직 균형을 위한 제어를 실현하였다. 또한 회전형 역 진자를 이용하여 PID 제어기로 제어가 가능한지 시뮬레이션과 실험을 통하여 그 타당성을 검증하였다.

• 동력기계공학회지
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• 제2권2호
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• 전기학회논문지
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• 제65권6호
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• pp.1089-1094
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• 2016

In order to establish the robust controller design technique of series wound motor driver system. This paper proposes a method of Bang-Bang controller using a series wound motor driver system under improperly variable load. A Bang-Bang controller structure is simpler than the structure of PID plus Bang-Bang controller. This paper shows that a general 8 bits microprocessor is used efficiently implementing such an algorithm. The calculation time of software is extremely small when compared with conventional PID plus Bang-Bang controller. Both nonlinear operating characteristics of digital switching elements and describing function methods are used for the analysis and synthesis. Real time implementation of Bang-Bang controller is achieved. Concept design strategy of the control and PWM waveform generation algorithms are presented in the paper.

• 대한기계학회논문집A
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• 제25권5호
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• pp.850-856
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• 2001

Even though the hydraulic servo system has been widely used in industrial and military equipments since it has a lot of advantages, it is not easy to design controller due to the high nonlinearities and the parametric uncertainties. The dynamic behavior of the real process in the hydraulic servo system differs from that described by its model because the model is linearized. Another reason of the difference is caused by the variety of parameters, since the system parameters of the dynamic equation are affected by the operating conditions such as temperature and pressure. In this study, the designing process of the MRNC with a PID compensator is introduced and applied to the load sensing hydraulic servo system. The results show that the designed controller guarantees the robust control performance despite of both the nonlinearities and the parametric uncertainties.

• 제어로봇시스템학회논문지
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• 제7권6호
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• International Journal of Automotive Technology
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• 제4권4호
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.416-419
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• 2001

In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.