• 제어로봇시스템학회논문지
• /
• 제6권11호
• /
• pp.1013-1021
• /
• 2000

This paper suggests fuzzy-PI controllers for a heavy-duty gas turbine. The fuzzy-PI controllers are designed to regulate rotor speed and exhaust temperature of the gas turbine. The controller gains are tuned by genetic algorithm(GA). This paper also proposes a new fitness function of GA using a desired output response. The suggested controller is compared with previous controllers via simulations and it is shown that the rotor speed variation of our controller is smaller than those of previous ones.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
• /
• pp.135-137
• /
• 1995

A new design method is proposed for a fuzzy PD controller. By analyzing phase plane characteristics we can build and optimize the rule base of fuzzy logic controller. Also, a new gain tuning method is used to improve performance in the transient and steady state. The improved performance of the new methodology is shown by an application to the design of control system with a highly nonlinear actuator.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 하계종합학술대회 논문집
• /
• pp.1121-1124
• /
• 1999

Linear motor is able to produce line movement without rotary-to-line converter at the system required line moving. Thus Linear motor has no gear, screw, belt for line movement. Therefore it has some advantage which decrease friction loss, noise, vibration, maintenance effort and prevent decay of control performance due to backlash. This paper proposes the estimation method of unknown parameters from the BLDC Linear motor and determine the PI controller gain through this estimation. Each control movement that is current, speed, position control, and PWM wave generation is performed on Processor, which is DSP(Digital Signal Processor), having high speed performance. PI theory is adopted to each for controller for control behavior More fast convergence to command position is accomplished by applying the new velocity locus which derived from position error.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2001년도 추계학술대회(한국공작기계학회)
• /
• pp.99-104
• /
• 2001

The neural network method has been introduced to design a controller for linear motor feed system and system operation status was monitored. It is most difficult to achieve controller gain tuning because of the information limit. Regardless of the system structure, conventional control gain could be adjusted minimizing the resulting error for both position and velocity using the proposed method. Slight performance deterioration was observed at the small value of training epoch. Different controller performance for position was observed with respect changed sampling time. Actuated system performance was monitored using neural network signal processing and operational status was predicted with the rate of 80% approximately.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제51권4호
• /
• pp.187-194
• /
• 2002

Recently, the application of the linear machine far industrial field is remarkable increased, especially for the gantry machine, machine tool system and CNC. In these application fields, high dynamics position control performance Is essentially required in both the steady and the transient state. This pacer presents simple but powerful position control loop based on traditional PID controller. The presented position control algorithm, named 'Unified PID Position Controller'has great features for the linear machine drives such as no over-shoot phenomena and simple gain tuning strategy. Through the experimental results with commercial linear motors, it is shown that the proposed algorithm has excellent dynamics suitable fur linear motions.

• KIEE International Transaction on Systems and Control
• /
• 제2D권2호
• /
• pp.78-91
• /
• 2002

In the thermal power plant, there are six manipulated variables: main steam flow, feedwater flow, fuel flow, air flow, spray flow, and gas recirculation flow. There are five controlled variables: generator output, main steam pressure, main steam temperature, exhaust gas density, and reheater steam temperature. Therefore, the thermal power plant control system is a multinput and output system. In the control system, the main steam temperature is typically regulated by the fuel flow rate and the spray flow rate, and the reheater steam temperature is regulated by the gas recirculation flow rate. However, strict control of the steam temperature must be maintained to avoid thermal stress. Maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature versus changes in fuel flow rate, difficulty of control of the main steam temperature control and the reheater steam temperature control system owing to the dynamic response characteristics of changes in steam temperature and the reheater steam temperature, and the fluctuation of inner fluid water and steam flow rates during the load-following operation. Up to the present time, the Proportional-Integral-Derivative Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper focuses on the characteristic comparison of the PID controller and the modified 2-DOF PID Controller (Two-Degrees-Freedom Proportional-Integral-Derivative) on the DCS (Distributed Control System). The method is to design an optimal controller that can be operated on the thermal generating plant in Seoul, Korea. The modified 2-DOF PID controller is designed to enable parameters to fit into the thermal plant during disturbances. To attain an optimal control method, transfer function and operating data from start-up, running, and stop procedures of the thermal plant have been acquired. Through this research, the stable range of a 2-DOF parameter for only this system could be found for the start-up procedure and this parameter could be used for the tuning problem. Also, this paper addressed whether an intelligent tuning method based on immune network algorithms can be used effectively in tuning these controllers.

• Journal of Advanced Marine Engineering and Technology
• /
• 제38권1호
• /
• pp.39-47
• /
• 2014

본 연구에서는, 자동으로 조작되는 밸브의 제어기로 사용되는 유압모션 제어기의 한계점인 설정치 추종성능과 외란 억제성능을 개선하기 위한 방법으로 기존의 PID 제어기에 피드-포워드 제어기를 추가한 2자유도(DOF) PID 제어기를 이용하였다. 이 제어기의 제어인자(Parameter)를 최적화시키는 도구로 실수코딩 유전알고리즘(Real Coded Genetic Algorithm, RCGA)을 이용하고 시뮬레이션을 통해 제어기의 성능을 검증하였다.

• 대한전기학회논문지:전력기술부문A
• /
• 제51권4호
• /
• pp.188-195
• /
• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLS[10] for three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[10].

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 제36회 하계학술대회 논문집 B
• /
• pp.1453-1455
• /
• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• 로봇학회논문지
• /
• 제17권1호
• /
• pp.76-85
• /
• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.