Intelligent Switching Control Of Pneumatic Cylinders By Learning Vector Quantization Neural Network
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Table of Contents - Scientific.Net
Study on Thermal Fatigue Life Prediction of Cylinder Head. W.M. Zhao and W.Z. Study on the Characteristics of a Pneumatic Spring with Bellow Intelligent Transportation Scheduling System Based on Fuzzy Control Converter with Soft Switch Controlling of Traffic Lights Using RFID Technology and Neural Network.
Power Quality Control of Smart Hybrid AC/DC Microgrids: An
by F Nejabatkhah Cited by 53 sections Ionizing radiation Unmanned autonomous vehicles Neural prosthesis Surface tension Holmium Displacement control Pneumatic systems Activity Sulfur Time-frequency analysis Service-oriented architecture Vector quantization Image databases Switching converters Cellular networks Magnetic sensors
JAKO200619129099224.pdf - Korea Science
by KK Aan 2006 Cited by 7 Therefore, intelligent switching control using Learning Vector Quantization Neural Network. (LVONN) is Key Words : Hybrid Actuator, Hydraulic System, Neural Network, Intelligent Control,. Switching including servo pump losses, cylinder losses and pressure ment of the Control Performance of Pneumatic. Artificial
COMPARISON OF SLIDING-MODE CONTROL - MacSphere
by Y Zhang 2015 valve life) when applied to a pneumatic cylinder with high friction seals. The designed SMC algorithm, and the valve switching frequency with DVMPC was 34% lower. The using a learning vector quantization neural network (LVQNN) to deal with different payloads. Intelligent switching control of pneumatic actuator.
WORKSHOP PROCEEDINGS - NASA Technical Reports Server
by I MOOEL 1994 Application of Adaptive Learning to Diagnostics: The Role of Neural Networks in Keynote Address: Neural Nets and Intelligent Control: A Strategic requirements of air intercept missiles are some of the most demanding were the individual cylinder torque traces, the overall torque trace, the The vector quantization.
RADIAL BASIS AND LVQ NEURAL NETWORK FOR REAL
by M Demetgul 2014 nonworking bottle cap closing cylinder C, insufficient air pressure, water not  tried to control a bottle filling plant Radial basis and LVQ neural network algorithm for real time fault diagnosis of electro-pneumatic valves, three limit switches, one step  Chen, C.; Mo, C. A method for intelligent fault diagnosis of.
CMAC Based Hybrid Control System for Solving - IGI Global
slip friction in valves and cylinders. In addition suitable intelligent control system that has the ability to deal with the system Model Articulation Controller (CMAC) neural network is presented. they overcome the problems of pneumatic Therefore, the allocating vector which is a nonlinear switching learning function.
An Electro-Pneumatic Force Tracking System using Fuzzy
by Z Lin 2019 Cited by 2 In modern society, especially in industry, pneumatic control systems which use total switching time for this valve is less than 1.5 ms for one PWM period. Ahn, K.; Yokota, S. Intelligent switching control of pneumatic actuator using of pneumatic cylinders by learning vector quantization neural network.
Accurate Position Control of Pneumatic Actuator - IJSRST
22 Feb 2017 consists a standard double acting cylinder controlled with two three-way solenoid valves through a 12-bit A/D PC board. Intelligent control for an electro-hydraulic actuator such as neural this paper was to apply the learning vector quantization neural network (LVQNN) as a supervisor of switching.
Literature Review and Problem Statement
The hydraulic actuator, usually a cylinder, converts the hydraulic power to The fourth category is the rapid on-off solenoid valve (i.e. fast switching valve). learning vector quantization neural network (LVQNN) has newly proposed,  presented a position-controlled pneumatic actuator using pulse width modulation.
Intelligent Assistive Knee Orthotic Device Utilizing Pneumatic
by MI Chandrapal 2012 Cited by 10 tigating improvements to neural network based EMG to joint torque estimation. Paladyn: Journal CHAPTER 5 CONTROL OF PNEUMATIC ARTIFICIAL MUSCLE 87 angle between the neutral axis on the shank and the gravity vector β The exoskeleton was actuated via linear bidirectional hydraulic cylinders that were.
Intelligent switching control of a pneumatic muscle - CiteSeerX
by KK Ahn 2007 Cited by 78 using learning vector quantization neural network Neural network; Switching control; Intelligent control; Pneumatic robot arm. 1. and pneumatic cylinders.
Download (4Mb) - warwick.ac.uk/lib-publications - University
by TQ Dinh 2017 Cited by 7 Ha, Cheolkeun. (2017) Sensorless force feedback joystick control for teleoperation of The slave employs an asymmetrically pneumatic cylinder as its manipulator. is built as a learning vector quantitative neural network (LVQNN) capable of scheme as learning vector quantization (LVQ) or self-organizing map. (SOM)
Redalyc.Simulation of Control of a Scara Robot Actuated by
by F Escobar 2014 Cited by 8 actuated by a pair of McKibben pneumatic artificial muscles. Keywords: Control Systems, Artificial Neural Networks, Multilayer Perceptron, Pneumatic Artificial electronic and pneumatic (pneumatic cylinder) Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network,.
Intelligent Electro-pneumatic Position Tracking - IEEE Xplore
by Z Lin 2018 Cited by 11 ABSTRACT In this paper, a mode-switching sliding controller with the nonlinear gain generated by a pneumatic control valve, which is regarded as the interface between cylinders by learning vector quantization neural network, Journal of.
Fault Detection and Diagnosis for A Multi-Actuator Pneumatic
parts in a pneumatic system, actuators, control valves and tubes, are discussed and extract- ed as system performance features in a quantitative study of leakage fault. vious fault analysis research in one-cylinder pneumatic system. neural networks for the purpose of fault detection and diagnosis in dynamic systems with.
Fault diagnosis and prognosis of hybrid systems - DR-NTU
by M Yu Cited by 1 using bond graph models and computational intelligence. Yu, Ming 2.33 HBG of with a causality assignment when the switch is OFF. techniques for FDI are artificial neural networks and expert systems. and learning vector quantization to rotating machine fault detection. Identification of pneumatic.
A Review of Pneumatic Actuators (Modeling and Control)
pneumatic actuators, such as piston-cylinder and rotary types. On the other hand, in intelligent soft arm control (ISAC) robot system the pneumatic actuator was used discontinuities associated with switching and results in a model tractable to for control parameters using a learning vector quantization neural network.
Artificial Intelligence and Machine Learning at raytheon
This document does not contain technology or technical data controlled under either the Artist's depiction of a deep neural network human-like, general intelligence AI (or air dominance in the battlespace becomes random forests, support vector machines, High efficiency zero-voltage switching (ZVS) assistance.
Online Self Tuning PID Control Using Neural Network - IJENS
pneumatic cylinder enhanced with STNPID controller. For step Index Term Pneumatic, digital valves, PID control, neural network, self-tuning and intelligent control. 1. These parameters are taken as inputs for the learning process Linear vector quantization neural network [LVQNN] used as switching algorithm for.
Closed-Loop PID Control with PWM technique of a Rotary Pneumatic Actuator cylinder and pneumatic motor. for control parameters using a learning vector quantization neural network (LVQNN) has newly proposed, pneumatic fast switching valve such as in Ref   Ahn, K. and Yokata, S. Intelligent switching.
Intelligent Assistive Knee Orthotic Device Utilizing Pneumatic
by M Chandrapal 2012 Cited by 10 tigating improvements to neural network based EMG to joint torque estimation. Paladyn: Journal CHAPTER 5 CONTROL OF PNEUMATIC ARTIFICIAL MUSCLE 87 angle between the neutral axis on the shank and the gravity vector β The exoskeleton was actuated via linear bidirectional hydraulic cylinders that were.
Artificial Intelligence Hybrid Learning Architecture for Malware
by YJ Chen Cited by 3 vehicles Switching converters Spark gaps Software defined networking Microwave Overlay networks Machine vector control Motion artifacts Power system faults films Rectifiers Biological neural networks Distance learning Transmission lines lasers Social intelligence Photoreceptors Air pollution Whole body imaging
Design and simulation of a distortion masking control
by GF Bravo Palacios 2015 Modeling of lengthy tube connecting the control valves and the cylinder. intellectual capacity that He has granted me, and for having added to all the previously Study the non-linear dynamics of a pneumatic system composed by proportional (1994) justified the application of neural network control schemes due to.
Experimental Characterization of Components for Active Soft
by M Wehner Cited by 49 of soft pneumatic actuators currently under development for active soft  K. Ahn, H. Nguyen, Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network, Mechatronics Missing: cylinders Must include: cylinders
High-Precision Hydraulic Pressure Control Based on Linear
by C Lv 2017 Cited by 85 fluid flow, leading to a linear control of hydraulic pressure. Although some switching algorithm using a learning vector quantization neural network was also developed. In , a sensor 1. pw is the wheel cylinder pressure, which is the load  K. Ahn and S. Yokota, Intelligent switching control of pneumatic actuator
Dynamical Adaptive Backstepping-Sliding Mode Control for
by N Sepehri 2016 ble acting pneumatic cylinder and antagonistic pneumatic artificial muscles Ahn and H. T. C. Nguyen, Intelligent switching control of a pneumatic mus- cle robot arm using learning vector quantization neural network, Mechatronics, 2007,.
Fuzzy Throttle and Brake Control for Platoons of Smart Cars
by H Kim 1995 Cited by 102 This logic switch avoids frequent oscillations between the throttle Figure 4: A neural-fuzzy system can learn and tune the fuzzy rules with a hybrid of Adaptive vector quantization (AVQ) systems adaptively cluster pattern data in a state space. of Smart Cars, in Fuzzy Sets, Neural Networks, and Soft Computing, R. R.
Intelligent Electro-Pneumatic Position Tracking - IEEE Xplore
by Z Lin 2018 Cited by 11 ABSTRACT In this paper, a mode-switching sliding controller with the nonlinear gain generated by a fuzzy logic pneumatic control valve, which is regarded as the interface models of the on-off valve and the cylinder are introduced firstly. by learning vector quantization neural network,'' J. Mech. Sci.
DESIGN AND VERIFICATION OF A NEW ENERGY - IAARC
by KK Ahn Cited by 1 addition, the boom/arm/or bucket cylinder is driven by a closed EHA system, so that potential energy accumulated at up positions can A control strategy for the 5-ton EELEX was built to operate the machine to Hence, a switching algorithm using a learning vector quantization neural network (LVQNN) was proposed and.
Utilization of a Neural Network to Improve Fuel Maps of an Air
by RF Young 2010 Cited by 4 original equipment engine control module (ECM), artificial neural network (ANN) five years old and all the input throughout my learning experiences, without this the cylinder compressing the air- fuel mixture, preparing them for ignition: identifying patterns in the data and making intelligent decisions based upon them.
Arduino-Based Controller for Sequence Development of
by SS Khaleel 2020 Cited by 2 represented using pneumatic cylinders that move according to the sequence used. The system is control boundaries utilizing a learning Vector. Quantization Neural Network (LVQNN) has recently proposed In 2015, the design and study of intelligent electric arc as a switch when the signal reaches it. As a result,.
Design, implementation and modelling of the single and
by A AlIbadi 2018 Cited by 13 Systems Science & Control Engineering, 6:1, 80-89, DOI: extensor pneumatic muscle actuator (PMA) which is depended on the Davis and Caldwell (2006) study the impact of the braided controller tuned by a back propagation neural network Intelligent switching con- quantization neural network.
Feedforward augmented Sliding Mode Motion Control of
by EH Skorina Cited by 35 soft pneumatic actuators towards a bio-inspired soft robotic vision. While soft the position of a pneumatic rodless cylinder. However, this for control parameters using a learning vector quantization neural network. In addition, , , 241 253, 1998.  K. Ahn and S. Yokota, Intelligent switching control of pneumatic.
2009 (Heisei 21) Doctoral Thesis MINIATURIZED - Hirai Lab.
2 Feb 2019 6 Unconstrained Valves for Control of Pneumatic Cylinders 98. 6.1 Introduction 5.16 Switching state diagram for two-level hysteresis pressure con- trol compliant robots, and an intelligent pneumatic cylinder. In robotic fields there is a a learning vector quantization neural network (LVQNN) .
A review on recent research trends in servo pneumatic
dynamics inside the pneumatic cylinder chambers, the frictional force variations and the compressed air required any special training in control system design to be oper- quantization neural network (LVQNN) as a supervisor of switch-  Ahn K, Yokota S. Intelligent switching control of pneumatic actuator using.
Modeling and Control of a Pneumatic Muscle Actuator - Tampere
Sliding Mode Controlled Pneumatic Muscle and Cylinder Actuator accepted for Figure A-9: Equivalent and switching control with perfect system model Learning Vector Quantization Neural Network. MIMO The intelligent control is a.
Executing synchronous dataflow graphs on a SPM-based
by J Choi Cited by 62 Iterative learning control Algorithm design and theory Powders Mobile ad hoc networks Through-silicon vias Baluns Nanosensors Optical switches Midbrain Teletext Vector quantization Ambient intelligence Optical coherence tomography Thomson Electrothermal launching Air pollution SRAM chips Network-on-chip
Design, implementation and modelling of the single and
by A Al-Ibadi 2018 Cited by 13 extensor pneumatic muscle actuator (PMA) which is depended on the constructed (NN) control system is applied to control the elongation of the extensor PMA. Davis and Caldwell (2006) study the impact of the braided controller tuned by a back propagation neural network Intelligent switching con-.
Artificial Neural Network Approaches in Guidance and Control
by AL SERIES 1991 guidance, navigation and control applications because of their ability to learn and aquin knowledge. Dekelia Air Force Base Intelligent Systems Division and Learning Vector Quantization (Kohonen, when the output represents a number, such as power levels or switch settings, etc. undersea metal cylinder and a.
by Learning Vector Quantization Neural Network
by KK Ahn 2005 Cited by 5 529-539, 2005 529. Intelligent Switching Control of Pneumatic Cylinders by Learning Vector Quantization Neural Network. KyoungKwan Ahn , ByungRyong Lee.
Energy consumption prediction using machine learning; a
11 Mar 2019 artificial intelligence (AI); computational intelligence (CI); forecasting; soft solar air heater predict and control power consumption in mineral mining industry. neural network for quantifying energy saving using measurement and energy systems: A comparison of support vector regression, random
Uporaba frekvenčno reguliranih ektromotorjev v hidravlični
by Ž Šitum Cited by 4 Pneumatic muscle actuators within robotic and mechatronic systems. Željko Šitum been dominated by pneumatic cylinders or motors.  Ahn, K.K., Nguyen, H.T.C., Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network, Mechatronics, 2007, 17, pp. 255 262.
Dynamic Control for a Pneumatic Muscle Actuator to Achieve
by KL Hall 2011 Cited by 7 18.104.22.168 Neural Networks Applied to Pneumatic Muscle Systems an intelligent pneumatic muscle using shape memory alloy wires in the around the end-cap is not a cylinder so an integration technique was used Ahn and Tu designed a switching algorithm using a learning vector quantization neural.
Investigations of Response Time Parameters of a Pneumatic 3
by KA Venkataraman 2013 Cited by 11 Pneumatic control systems provide low cost automation to the industry with the servo-pneumatic switching valve has been widely used in the industry [2-4]. pneumatic cylinder in . learning vector quantization neural networks (LVQNN). The  K. Ahn, S. Yokota, Intelligent switching control of pneumatic actuators.
Intelligent switching control of pneumatic cylinders by learning
by KK Ahn 2005 Cited by 6 by Learning Vector Quantization Neural Network Key Words : Pneumatic, Switching Control, On/off Solenoid Valve, Pulse Width Modulatmn,. Neural Network
The handbook of brain theory and neural networks - X-Files
Artifical Intelligence and Neural Networks 113. Contents Learning Vector Quantization 631. Lesioned land, air, or sea, etc.). network to switch dramatically its overall mode of activity. function of both the state q(t) and the input or control vector u(t) which are generalized cylinders formed by moving a cross-section.
Intelligent Switching Control of Pneumatic Artificial - J-Stage
by KK AHN 2005 Cited by 20 Key Words: Pneumatic Artificial Muscle, Neural Network, Switching Control, Intelligent. Control. 1. Introduction. Actuator In order to overcome these problems, learning vector quantization neural network (LVQNN) is applied as a su- pervisor of the sition control of pneumatic rodless cylinder that LVQNN is the appropriate
One Nonlinear PID Control to Improve the Control
by J Zhong 2014 Cited by 18 A switching control approach using learning vector quantization neural networks was designed Figure 5: Time histories of the applied pressure to pneumatic cylinder, the measured output displacements and contracting forces, and the better choice is to use nonparametric intelligent controller.
Intelligent Switching Control of the Pneumatic Artificial Muscle
by KK Ahn 2004 Cited by 20 position control of the pneumatic rodless cylinder that the. LVQNN was an appropriate Keywords: pneumatic artificial muscle, neural network, switching control, intelligent control. 76 Here we propose the learning vector quantization neural