• 대한전기학회논문지:시스템및제어부문D
• /
• 제50권3호
• /
• pp.107-113
• /
• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• 산업기술연구
• /
• 제18권
• /
• pp.303-308
• /
• 1998

In this paper a neural network controller called "Feedback-State Learning" for control of the attitude of a wheeled inverted pendulum is presented. For the controller the design of a stable feedback controller is necessary, so the LQR is used for the feedback controller because the LQR has good performance on controlling nonlinear systems. And the neural networks are used for a feed forward controller. The designed controller is applied to the stabilization of a wheeled inverted pendulum. Because of its nonlinear characteristics such as friction and parameter variations in the linearization, the wheeled inverted pendulum is used for demonstration of the effectiveness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.67.1-67
• /
• 2001

This paper presents a neural network based self-tuning fuzzy PI-PD control scheme for robust speed control of induction motor. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PI-PD controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small ...

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.861-865
• /
• 1991

In general, the fusion of neural network and fuzzy logic theory is based on the fact that neural network and fuzzy logic theory have the common properties that 1) the activation function of a neuron is similar to the membership function of fuzzy variable, and 2) the functions of summation and products of neural network are similar to the Max-Min operator of fuzzy logics. In this paper, a fuzzy-neural network will be proposed and a force controller of the robot gripper, utilizing the fuzzy-neural network, will be presented. The effectiveness of the proposed strategy will be demonstrated by computer simulation.

• International Journal of Control, Automation, and Systems
• /
• 제3권4호
• /
• pp.552-563
• /
• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 융합신호처리학회논문지
• /
• 제13권4호
• /
• pp.233-237
• /
• 2012

본 논문에서는 RTDNN과 FLC를 이용해서 주신경망을 보상하는 제어시스템을 제안한다. 주신경망이 학습을 완료한 후 외란이나 부하변동이 생겨 오브 슛 내지는 언더 슛을 나타낼 때 적절히 조정하기 위해 퍼지 보상기를 사용하여 원하는 결과를 얻을 수 있도록 하였다. 그리고 제어대상의 역모델 신경망에서 학습시킨 결과를 이용하여 주신경망의 가중치를 변경시킴으로서 제어대상의 원하는 동적 특성을 얻게 된다. 모의 실험 결과 제안한 신경망 제어기의 양호한 응답 특성을 확인 할 수 있다.

• 한국정보통신학회논문지
• /
• 제16권2호
• /
• pp.228-236
• /
• 2012

본 논문에서는 빠르고 조작이 간편한 이동 수단인 이륜 역진자 로봇을 기존의 방법보다 더욱 안정적으로 밸런싱하기 위한 제어기를 설계하였다. 먼저 이륜 역진자 로봇의 제어기를 퍼지제어 구조로 선택하고, 지정된 3명의 사용자 무게에 따라 적절한 소속함수 요소 값들을 시행착오적으로 구하였다. 임의의 무게에 대한 적절한 퍼지 소속함수 요소 값을 구하기 위해 앞의 3명의 무게에 따른 퍼지 소속함수 요소 값들을 신경회로망으로 튜닝한 뒤 퍼지 제어기에 적용하여 보다 안정적인 제어가 가능하도록 제어기를 설계하였다. 설계된 제어기를 시뮬레이션 하여본 결과, 기존의 퍼지 제어기에 비해서 본 논문에서 제안한 신경회로망으로 튜닝한 퍼지제어기가 보다 안정적인 제어가 가능함을 확인할 수 있었다.

• 전자공학회논문지SC
• /
• 제41권4호
• /
• pp.21-28
• /
• 2004

본 논문에서는 다변수 비선형 시스템에 적응할 수 있는 신경회로망을 이용한 직접 다변수 자기동조 제어기를 제안한다. 제어기에 적용되는 플랜트는 고차이고 잡음, 시간지연과 상호결합 항이 존재하며 파라미터가 변하는 다변수 비선형 비최소위상 시스템이다. 비선형성은 전체적인 유계라 가정하며, 시스템은 선형부분과 비선형부분으로 분리한 형태로 구성한다. 다변수 비선형 자기동조 제어기의 제어 출력은 신경회로망으로 직접 추정된 제어기 파라미터로부터 얻어진다. 제어 알고리듬의 타당성을 확인하기 위해 시간지연이 있고 일정한 시간이 경과한 후 시스템의 파라미터가 변하는 고차 다변수 비선형 비최소위상 시스템에 대해 컴퓨터 시뮬레이션을 하였다. 그리고 신경회로망을 이용한 직접 다변수 적응 제어기와 비교하였다.

• 제어로봇시스템학회논문지
• /
• 제3권4호
• /
• pp.363-372
• /
• 1997

In this paper, the design and the implementation of the adaptive learning rate neural network controller for an articulate robot, which is being developed (or) has been developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies hardware structures by the time-division control with TMS32OC31 DSP chip. Proposed neural network controller with adaptive learning rate structure using expert's heuristics can improve learning speed. The proposed controller verifies its superiority by comparing response characteristics of conventional controller with those of the proposed controller that are obtained from the experiments for the 5 axis vertical articulated robot. We, also, present the generalization property of proposed controller for unlearned trajectory and the change of load through experimental data.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
• /
• 제55권2호
• /
• pp.89-97
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.