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neural network controller with on-line inventory feedback data in RFID-enabled supply chain

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2010年第9期48卷 2613-2632页

作者： Hong, Seong Rok Kim, Shin Tae Kim, Chang Ouk Yonsei Univ Dept Informat & Ind Engn Seoul 120749 South Korea

The item-level visibility which can be secured by RFID technology can help the inventory records of a supply chain correspond closer to the actual inventories. More accurate and timely tracking of chain-wide inventories provides a great potential for optimised on-line control of supply chains. In this paper, we develop an on-line neural network controller that optimises a three-stage supply chain. With the inventory data feedback from an RFID system, the neural network controller minimises the total cost of the supply chain rapidly while satisfying a target order fulfilment ratio. As a test bed of the neural network controller, we develop the beer game model of the supply chain. We demonstrate through simulation-based experiments that the neural network controller shows the highest performance when the inventory data is secured from item-level RFID data.

关键词： RFID supply chain control neural network controller amplification error function tracking signal

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neural network controller for nanopositioning of a smooth impact drive mechanism

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第1期27卷 663-674页

作者： Lu, Xiaohui Chen, Dong Cheng, Tinghai Li, Zhe Changchun Univ Technol Sch Mechatron Engn Changchun Jilin Peoples R China

In this paper, neural network theory is used to improve the positioning accuracy of smooth impact drive mechanisms (SIDMs), by designing a displacement controller that consists of a neural network identification (NNI) and a neural network controller (NNC). The dynamics of the SIDM are described by the NNI, which consists of an input layer, hidden layer, and output layer. The parameters of the NNI are adjusted using back propagation. The NNC is designed as a proportional-derivative (PD) controller, which is used to accurately control the displacement of the SIDM. The PD parameters are adjusted with an adaptive adjustment algorithm. A prototype of the SIDM was fabricated and an experimental control system was built that consists of a laser displacement sensor, power amplifier, data acquisition board, and SIDM prototype. The experimental results show that nanoscale positioning accuracy can be obtained. The control system can maintain steady operation, even if the output load mass is changed.

关键词： Smooth impact drive mechanism nanopositioning accuracy neural network controller experiment system

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neural network controller for cooperating robots

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ELECTRONICS LETTERS 2001年第22期37卷 1351-1352页

作者： Yildirim, S Ereiyes Univ Mech Engn Div Robot Res Lab Kayseri 38039 Turkey

A co-ordinated position/force control system for two robot arms using neurocontrollers is proposed, The robots had to carry a load along a prescribed trajectory. Each robot is a SCARA arm with two driven joints. Simul... 详细信息

关键词： dexterous manipulators prescribed trajectory neurocontrollers force control position control system second-order linear position reference model neural network controller simulation results industrial manipulators neural-position controller driven joints force control system SCARA arm Mechanical variables control Manipulators robot arms path planning recurrent hybrid neural networks co-ordinated position/force control system position control Spatial variables control Neurocontrol end effector cooperating robots

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neural network controller for Regulation of a Water-Cooled Fuel Cell Stack 15

Neural Network Controller for Regulation of a Water-Cooled F...

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15th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Misbahuddin, Syed El-Sharkh, M. Y. Palanki, Srinivas Univ S Alabama Coll Engn Mobile AL 36688 USA Lamar Univ Coll Engn Beaumont TX 77710 USA

ISBN: (纸本)9781509061679

In this paper, a backpropagation neural network is designed to control the temperature of a 5 kW hydrogen fuel cell stack. This controller is initially trained to recognize the pattern of dynamic power demand from a consumption source. The power demand is the driving force in current production from the fuel cell, which in turn increases the temperature of the fuel cell stack. This temperature change is controlled by changing the flow rate of cooling water through the fuel cell stack. The results show that the neural network controller has excellent performance in maintaining the stack temperature at the desired set-point despite significant fluctuations in power demand.

关键词： Water-Cooled Fuel Cell Stack neural network controller

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neural network controller FOR ADAPTIVE ROUTING IN SURVIVABLE COMMUNICATIONS networkS

NEURAL NETWORK CONTROLLER FOR ADAPTIVE ROUTING IN SURVIVABLE...

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INTERNATIONAL JOINT CONF ON neural networkS ( IJCNM-90 )

作者： JENSEN, JE ESHERA, MA BARASH, SC Martin Marietta Lab Baltimore MD USA

An isolated routing algorithm for adaptive dynamic routing in communications networks that is based on a neural network approach has been developed. A neural network is located within each site of a communications network, and it uses the patterns of incoming traffic to maintain and dynamically update an internal representation of the global topology of the communications network. The information stored in the neural network models is updated using various routing paradigms, including Hebbian learning. The scheme has been implemented in a software simulation running on a Sun workstation

关键词： neural network controller internal representation updating telecommunication traffic patterns survivable communications networks isolated routing algorithm adaptive dynamic routing global topology Hebbian learning software simulation Sun workstation adaptive control controllers digital simulation knowledge representation network topology neural nets telecommunications computer control

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neural network controller Design for a Mobile Robot Navigation;a Case Study 4

Neural Network Controller Design for a Mobile Robot Navigati...

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4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Dewi, Tresna Risma, Pola Oktarina, Yurni Roseno, M. Taufik Politeknik Negeri Sriwijaya Dept Elect Engn Palembang 30139 Indonesia

ISBN: (纸本)9781538605493

Mobile robot are widely applied in various aspect of human life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task when applied autonomously in dynamic and uncertain environment. The application of artificial intelligence, namely neural network, can provide a "brain" for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be reduced by choosing the right model of the system, either from mathematical modeling or directly taken from the input of sensory data information. In this study, we compare the presented methods of previous researches that applies neural network to mobile robot navigation. The comparison is started by considering the right mathematical model for the robot, getting the Jacobian matrix for online training, and giving the achieved input model to the designed neural network layers in order to get the estimated position of the robot. From this literature study, it is concluded that the consideration of both kinematics and dynamics modeling of the robot will result in better performance since the exact parameters of the system are known.

关键词： Dynamics modeling kinematics modeling mobile robot navigation neural network controller

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Design and implementation of a neural network controller for real-time adaptive voltage regulation

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NEUROCOMPUTING 2009年第1-3期73卷 531-535页

作者： Yu, Xiao-Hua Li, Weiming Taufik Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA 93407 USA

An adaptive controller based on multi-layer feed-forward neural network is developed for real-time voltage regulation of a class of PSFB (phase-shifted full-bridge) DC-DC converters. The controller has the unique advantages of nonlinear mapping and adaptive learning, and performs well over a wide range of input voltages and output load currents. The controller is implemented and tested in hardware using a DSP (digital signal processor) board. Experimental results show that it outperforms conventional controllers in both line regulation and load regulation. (C) 2009 Elsevier B.V. All rights reserved.

关键词： neural network controller DC-DC voltage converter neural network hardware

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Optimization algorithm-based Elman neural network controller for continuous stirred tank reactor process model

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CURRENT SCIENCE 2021年第8期120卷 1324-1333页

作者： Baranilingesan, I KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore 641407 Tamil Nadu India

A continuous stirred tank reactor (CSTR) is a standout nonlinear system among the most essential units of chemical industries. In this article, an Elman neural network is designed to analyse the characteristics of nonlinear behaviour of the CSTR system. The data generated employing the state-space model of CSTR are used to train the designed Elman neural network controller and the controller parameters are optimally tuned by the proposed hybrid swarm intelligence-based optimization algorithm. Two different hybridizations have been developed, including DPSO, DGSA and hybrid DPSO-DGSA and successfully employed in controller tuning. The significance of the proposed controller is validated by a comparative analysis made with conventional methods and the performance is experimentally demonstrated using MATLAB software.

关键词： Continuous stirred tank reactor neural network controller nonlinear behaviour optimization algorithm process control

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A continually online-trained neural network controller for brushless DC motor drives

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2000年第2期36卷 475-483页

作者： Rubaai, A Kotaru, R Kankam, MD Howard Univ Dept Elect Engn Washington DC 20059 USA NASA John Glenn Res Ctr Power & On Board Prop Technol Div Cleveland OH 44135 USA

In this paper, a high-performance controller with simultaneous online identification and control is designed for brushless de motor drives, The dynamics of the motor/load are modeled "online," and controlled using two different neural network based identification and control schemes, as the system is in operation. In the first scheme, an attempt is made to control the rotor angular speed, utilizing a single three-hidden-layer network. The second scheme attempts to control the stator currents, using a predetermined control law as a function of the estimated states. This scheme incorporates three multilayered feedforward neural networks that are online trained, using the Levenburg-Marquadt training algorithm, The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories after relatively short online training periods, The control strategy adapts to the uncertainties of the motor/load dynamics and, in addition, learns their inherent nonlinearities. Simulation results illustrated that a neurocontroller used in conjunction with adaptive control schemes can result in a flexible control device which may be utilized in a wide range of environments.

关键词： adaptive learning high-performance drives neural network controller online training

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Hardware implementation of a real-time neural network controller with a DSP and an FPGA for nonlinear systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2007年第1期54卷 265-271页

作者： Jung, Seul Kim, Sung su Chungnam Natl Univ Dept Mechatron Engn Intelligent Syst & Emot Engn Lab Taejon 305764 South Korea

In this paper, we implement the intelligent neural network controller hardware with a field programmable gate array (FPGA)-based general purpose chip and a digital signal processing (DSP) board to solve nonlinear system control problems. The designed intelligent control hardware can perform real-time control of the backpropagation learning algorithm of a neural network. The basic proportional-integral-derivative (PID) control algorithms are implemented in an FPGA chip and a neural network controller is implemented in a DSP board. By using a high capacity of an FPGA chip, the additional hardware such as an encoder counter and a pulsewidth modulation (PWM) generator is implemented in a single FPGA chip. As a result, the controller becomes cost effective. It was tested for controlling nonlinear systems such as a robot finger and an inverted pendulum on a moving cart to show performance of the controller.

关键词： digital signal processing (DSP) field programmable gate array (FPGA) inverted pendulum neural network controller proportional-integral-derivative (PID) controller robot finger

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