• Title/Summary/Keyword: Neuro Systems

Search Result 317, Processing Time 0.024 seconds

Experimental Studies of a Fuzzy Controller Compensated by Neural Network for Humanoid Robot Arms (다관절 휴머노이드 상체 로봇의 제어를 위한 신경망 보상 퍼지 제어기 구현 및 실험)

  • Song, Deok-Hui;Noh, Jin-Seok;Jung, Seul
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.13 no.7
    • /
    • pp.671-676
    • /
    • 2007
  • In this paper, a novel neuro-fuzzy controller is presented. The generic fuzzy controller is compensated by a neural network controller so that an overall control structure forms a neuro-fuzzy controller. The proposed neuro-fuzzy controller solves the difficulty of selecting optimal fuzzy rules by providing the similar effect of modifying fuzzy rules simply by changing crisp input values. The performance of the proposed controller is tested by controlling humanoid robot arms. The humanoid robot arm is analyzed and implemented. Experimental studies have shown that the performance of the proposed controller is better than that of a PID controller and of a generic fuzzy PD controller.

Robust Control of Robot Manipulator Based-on DSPs(TMS320C50) (DSPs(TMS320C50)을 이용한 로봇 매니퓰레이터의 견실제어)

  • 이우송;김종수;김홍래;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 2004.10a
    • /
    • pp.193-200
    • /
    • 2004
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

  • PDF

A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering

  • Ryu, Jeong-Woong;Song, Chang-Kyu;Kim, Sung-Suk;Kim, Sung-Soo
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.5 no.2
    • /
    • pp.95-101
    • /
    • 2005
  • In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

Neuro-fuzzy Control for Balancing a Two-wheel Mobile Robot (이륜구동 이동로봇의 균형을 위한 뉴로 퍼지 제어)

  • Park, Young Jun;Jung, Seul
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.22 no.1
    • /
    • pp.40-45
    • /
    • 2016
  • This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

Implementation of the Adaptive-Neuro Control of Robot Manipulator Using DSPs(TMS320C50) (DSPs(TMS320C50)를 이용한 로봇 매니퓰레이터의 적응-신경제어기 실현)

  • 정동연;김용태;한성현
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
    • /
    • 2002.10a
    • /
    • pp.256-261
    • /
    • 2002
  • In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

  • PDF

Short-term Electrical Load Forecasting Using Neuro-Fuzzy Model with Error Compensation

  • Wang, Bo-Hyeun
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.9 no.4
    • /
    • pp.327-332
    • /
    • 2009
  • This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

A Study on the Feedforward Neural Network Based Decentralized Controller for the Power System Stabilization (전력계토 안정화 제어를 위한 신경회로만 분산체어기의 구성에 관한 연구)

  • 최면송;박영문
    • The Transactions of the Korean Institute of Electrical Engineers
    • /
    • v.43 no.4
    • /
    • pp.543-552
    • /
    • 1994
  • This paper presents a decentralized quadratic regulation architecture with feedforward neural networks for the control problem of complex systems. In this method, the decentralized technique was used to treat several simple subsystems instead of a full complex system in order to reduce training time of neural networks, and the neural networks' nonlinear mapping ability is exploited to handle the nonlinear interaction variables between subsystems. The decentralized regulating architecture is composed of local neuro-controllers, local neuro-identifiers and an overall interaction neuro-identifier. With the interaction neuro-identifier that catches interaction characteristics, a local neuro-identifier is trained to simulate a subsystem dynamics. A local neuro-controller is trained to learn how to control the subsystem by using generalized Backprogation Through Time(BTT) algorithm. The proposed neural network based decentralized regulating scheme is applied in the power System Stabilization(PSS) control problem for an imterconnected power system, and compared with that by a conventional centralized LQ regulator for the power system.

Design of intelligent control strategies using a magnetorheological damper for span structure

  • Hernandez, Angela;Marichal, Graciliano N.;Poncela, Alfonso V.;Padron, Isidro
    • Smart Structures and Systems
    • /
    • v.15 no.4
    • /
    • pp.931-947
    • /
    • 2015
  • This paper focuses on the design of an intelligent control system. The used techniques are based on Neuro Fuzzy approaches applied to a magnetorheological damper in order to reduce the vibrations over footbridges; it has been applied to the Science Museum Footbridge of Valladolid, particularly. A model of the footbridge and of the damper has been built using different simulation tools, and a successful comparison with the real footbridge and the real damper has been carried out. This simulated model has allowed the reproduction of the behaviour of the footbridge and damper when a pedestrian walks across the footbridge. Once it is determined that the simulation results are similar to real data, the control system is introduced into the model. In this sense, different strategies based on Neuro Fuzzy systems have been studied. In fact, an ANFIS (Artificial Neuro Fuzzy Inference System) method has also been used, in addition to an alternative Neuro Fuzzy approach. Several trials have been carried out, using both techniques, obtaining satisfactory results after using these techniques.

Neuro-Fuzzy Controller Based on Reinforcement Learning (강화 학습에 기반한 뉴로-퍼지 제어기)

  • 박영철;심귀보
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.10 no.5
    • /
    • pp.395-400
    • /
    • 2000
  • In this paper, we propose a new neuro-fuzzy controller based on reinforcement learning. The proposed system is composed of neuro-fuzzy controller which decides the behaviors of an agent, and dynamic recurrent neural networks(DRNNs) which criticise the result of the behaviors. Neuro-fuzzy controller is learned by reinforcement learning. Also, DRNNs are evolved by genetic algorithms and make internal reinforcement signal based on external reinforcement signal from environments and internal states. This output(internal reinforcement signal) is used as a teaching signal of neuro-fuzzy controller and keeps the controller on learning. The proposed system will be applied to controller optimization and adaptation with unknown environment. In order to verifY the effectiveness of the proposed system, it is applied to collision avoidance of an autonomous mobile robot on computer simulation.

  • PDF

Implementation of the Adaptive-Neuro Controller of Industrial Robot Using DSP(TMS320C50) Chip (DSP(TMS320C50) 칩을 사용한 산업용 로봇의 적응-신경제어기의 실현)

  • 김용태;정동연;한성현
    • Transactions of the Korean Society of Machine Tool Engineers
    • /
    • v.10 no.2
    • /
    • pp.38-47
    • /
    • 2001
  • In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

  • PDF