• Title/Summary/Keyword: neural network compensator

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Design of PID Controller with Adaptive Neural Network Compensator for Formation Control of Mobile Robots (이동 로봇의 군집 제어를 위한 PID 제어기의 적응 신경 회로망 보상기 설계)

  • Kim, Yong-Baek;Park, Jin-Hyun;Choi, Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.3
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    • pp.503-509
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    • 2014
  • In this paper, a PID controller with adaptive neural network compensator is proposed to control the formations of mobile robot. The control system is composed of a kinematic controller based on the leader-following robot and dynamic controller for considering the dynamics of the mobile robot. The dynamic controller is constituted by a PID controller and the adaptive neural network compensator for improving the performance and compensating the change in dynamic characteristics. Simulation results show the performance of the PID controller and the neural network compensator for the circular trajectory and linear trajectory. And it is verified that by improving the performance of a PID controller via the adaptive neural network compensator, the following robot's tracking performance is improved.

Design of Recurrent Time Delayed Neural Network Controller Using Fuzzy Compensator (퍼지 보상기를 사용한 리커런트 시간지연 신경망 제어기 설계)

  • 이상윤;한성현;신위재
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2002.04a
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    • pp.463-468
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    • 2002
  • In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.

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Nonlinear Friction Compensator Design for Mechatronics Servo Systems Using Neural Network

  • Chung, Dae-won;Nobuhiro Kyra;Hiromu Gotanda
    • Transactions on Control, Automation and Systems Engineering
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    • v.3 no.2
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    • pp.111-116
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    • 2001
  • A neural network compensator for stick-slip friction phenomena in meashartonics servo systems is practically proposed to supplement the traditionally available position and velocity control loops for precise motion control. The neural network compensa-tor plays the role of canceling the effect of nonlinear slipping friction force. It works robustly and effectively in a real control system. This enables the mechatronics servo systems to provide more precise control in the digital computer. It was confirmed that the con-trol accuracy is improved near zero velocity and points of changing the moving direction through numerical simulation. However, asymptotic property on the steady state error of the normal operation points is guaranteed by the integral term of traditional velocity loop controller.

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Design and Implementation of Neural Network Controller with a Fuzzy Compensator for Hydraulic Servo-Motor (유압서보모터를 위한 퍼지보상기를 갖는 신경망제어기 설계 및 구현)

  • 김용태;이상윤;신위재;유관식
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • 2001.06a
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    • pp.141-144
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    • 2001
  • In this paper, we proposed a neural network controller with a fuzzy compensator which compensate a output of neural network controller. Even if learn by neural network controller, it can occur a bad results from disturbance or load variations. So in order to adjust above case. we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning an inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. In order to confirm a performance of the proposed controller, we implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

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Friction Compensation For High Precision Control of Servo Systems Using Adaptive Neural Network

  • Chung, Dae-Won
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.179-179
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    • 2000
  • An adaptive neural network compensator for stick-slip friction phenomena in servo systems is proposed to supplement the traditionally available position and velocity control loops for precise motion control. The neural network compensator plays a role of canceling the effect of nonlinear slipping friction force. This enables the mechatronic systems more precise control and realistic design in the digital computer. It was confirmed that the control accuracy is more improved near zero velocity and the points of changing the moving direction through numerical simulation

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A Study on the Soiution of Inverse Kinematic of Manipulator using Self-Organizing Neural Network and Fuzzy Compensator (퍼지 보상기와 자기구성 신경회로망을 이용한 매니퓰레이터의 역기구학 해에 관한 연구)

  • 김동희;이수흠;신위재
    • Journal of the Institute of Convergence Signal Processing
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    • v.2 no.3
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    • pp.79-85
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    • 2001
  • We obtain a solution of inverse kinematic of 3 axis manipulator by using a self-organizing neral network(SONN) with a fuzzy compensator. The self-organizing neural network using the gaussian potential function as the activation function has one hidden layer in the first learning time. The network obtains the optimal number of node by increasing the number of hidden layer node through the learning, and the fuzzy compensator has the optimal loaming rate of neutral network. In this results, we can confirmed that the learning rate is improved and the rapid convergence to the steady-state.

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Design and Implementation of Recurrent Time Delayed Neural Network Controller Using Fuzzy Compensator (퍼지 보상기를 사용한 리커런트 시간지연 신경망 제어기 설계 및 구현)

  • Lee, Sang-Yun;Shin, Woo-Jae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.334-341
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    • 2003
  • In this paper, we proposed a recurrent time delayed neural network(RTDNN) controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network(TDU) controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

Design of Hybrid Controller Using Neural Network-Fuzzy (신경망-퍼지 하이브리드 제어기 설계)

  • 신위재
    • Journal of the Institute of Convergence Signal Processing
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    • v.3 no.1
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    • pp.54-60
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    • 2002
  • In this paper, we proposed a hybrid neural network-fuzzy controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of loaming a inverse model neural network of Plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed speed controller get a good response compare with a neural network controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

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Design of a sliding Mode Controller Using a Neural Compensator (신경회로망 보상기를 이용하는 슬라이딩 모드 제어기 설계)

  • Lee, Min-Ho;Jung, Soon-Ki
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.3
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    • pp.256-262
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    • 2000
  • This paper proposes a new sliding mode controller combined with a multi-layer neural network using the error back propagation learning algorithm,, The network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are violated. The proposed controller can reduce th steady state error of conventional sliding mode controller with the boundary layer technique Computer simulation results show that the proposed method is effective to control dynamic systems with unexpectably large uncertainties.

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Precision Position Control of PMSM Using Neural Network Disturbance observer and Parameter compensator (신경망 외란관측기와 파라미터 보상기를 이용한 PMSM의 정밀 위치제어)

  • 고종선;진달복;이태훈
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.53 no.3
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    • pp.188-195
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    • 2004
  • This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.