Impedance control with on-line neural-network compensator for robot contact tasks

1996 ◽  
Vol 15 (4) ◽  
pp. 389-399 ◽  
Author(s):  
Shih-Tin Lin ◽  
Jien-Shuin Lee
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
On Line ◽  
Contact Tasks ◽  
Neural Network Compensator

Adaptive Impedance Control for Robot Based on Estimation of Environmental Parameters

2011 ◽  
Vol 328-330 ◽  
pp. 1713-1716 ◽  
Author(s):  
Zhan Ming Li ◽  
Er Chao Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Impedance Control ◽  
Environmental Parameters ◽  
Changing Environment ◽  
Unknown Environment ◽  
Adaptive Impedance Control ◽  
Impedance Parameters ◽  
The One ◽  
Contact Tasks

In order to realize precise contact tasks with an unknown environment, robotic force controllers have to adapt themselves to the unknown environment. Some impedance controllers are designed for several representative environmental parameters, A BP neural network is proposed to determine the one-to-one mapping relations between the environmental parameters and the impedance parameters. However, it is difficult to accurately know the environmental parameters in the case of a changing environment, RLS is proposed to estimate environmental parameters, then determine the impedance coefficients to control the robot. Simulations prove that the controller designed is feasible and effective.

scholarly journals A Novel DSP-Based MPPT Control Design for Photovoltaic Systems Using Neural Network Compensator

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3260
Author(s):  
Ming-Fa Tsai ◽  
Chung-Shi Tseng ◽  
Kuo-Tung Hung ◽  
Shih-Hua Lin
Keyword(s):  
Neural Network ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Irradiation ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Irradiation Cell ◽  
Power Point ◽  
Neural Network Compensator

In this study, based on the slope of power versus voltage, a novel maximum-power-point tracking algorithm using a neural network compensator was proposed and implemented on a TI TMS320F28335 digital signal processing chip, which can easily process the input signals conversion and the complex floating-point computation on the neural network of the proposed control scheme. Because the output power of the photovoltaic system is a function of the solar irradiation, cell temperature, and characteristics of the photovoltaic array, the analytic solution for obtaining the maximum power is difficult to obtain due to its complexity, nonlinearity, and uncertainties of parameters. The innovation of this work is to obtain the maximum power of the photovoltaic system using a neural network with the idea of transferring the maximum-power-point tracking problem into a proportional-integral current control problem despite the variation in solar irradiation, cell temperature, and the electrical load characteristics. The current controller parameters are determined via a genetic algorithm for finding the controller parameters by the minimization of a complicatedly nonlinear performance index function. The experimental result shows the output power of the photovoltaic system, which consists of the series connection of two 155-W TYN-155S5 modules, is 267.42 W at certain solar irradiation and ambient temperature. From the simulation and experimental results, the validity of the proposed controller was verified.

scholarly journals AN ON-LINE NON-LEPTONIC NEURAL TRIGGER APPLIED TO AN EXPERIMENT LOOKING FOR BEAUTY

1994 ◽  
Vol 05 (05) ◽  
pp. 863-870
Author(s):  
C. BALDANZA ◽  
F. BISI ◽  
A. COTTA-RAMUSINO ◽  
I. D’ANTONE ◽  
L. MALFERRARI ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Enrichment Factor ◽  
Data Stream ◽  
Particle Production ◽  
Event Sample ◽  
On Line

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV π− on a Cu target, are presented. The neural trigger has been used to send on a special data stream (the Fast Stream) events to be analyzed with high priority. The non-leptonic signature uses microvertex detector data and was devised so as to enrich the fraction of events containing C3 secondary vertices (i.e, vertices having three tracks whith sum of electric charges equal to +1 or -1). The neural trigger module consists of a VME crate hosting two ETANN analog neural chips from Intel. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6–7.1 (depending on the event sample considered), which multiplied by that already provided by the standard non-leptonic trigger leads to a global C3 enrichment factor of ≈150. In the event sample selected by the neural trigger for the Fast Stream, 1 every ≈7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs.

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