Neural Network Control of a Flexible Link Manipulator in Contact With a Compliant Environment

2010 ◽  
Author(s):  
Reza Sabzehgar ◽  
Mehrdad Moallem
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
A Priori ◽  
Network Control ◽  
Model Parameters ◽  
Flexible Link ◽  
System A ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

A neural network controller for regulating the contact force of a flexible link manipulator in contact with a compliant environment is proposed in this paper. The dynamic model of a single-link flexible (SLF) manipulator is obtained using three rigid sub-links connected by two virtual springs. It is assumed that the length of each link is short enough to be considered as a rigid link. A neural network-based control strategy is then proposed to relax the a-priori knowledge of the model parameters of the flexible link manipulator. The weights of the neural network controller are adjusted to minimize the error between the actual contact force and desired force. To overcome the non-minimum phase characteristic of the system, a weighted term of input signal is added to controller’s cost function. Simulation results are presented to evaluate performance of the proposed controller.

Design and Realization of Experimental Autonomous Driving System Based on Neural Network Control

2012 ◽  
Vol 241-244 ◽  
pp. 1953-1958
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jie Chang Wu ◽  
Jia Ming Wu
Keyword(s):  
Neural Network ◽  
Autonomous Driving ◽  
Network Control ◽  
Neural Network Control ◽  
Test Results ◽  
Driving System ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Autonomous Driving System

Intelligent vehicle is an attractive solution to the traffic problems caused by automobiles. An experimental autonomous driving system based on a slot car set is designed and realized in the paper. With the application of a wireless camera equipped on the slot car, the track information is acquired and sent to the controlling computer. A backpropogation (BP) neural network controller is built to imitate the way of player’s thinking. After being trained, the neural network controller can give the proper voltage instructions to the direct current (DC) motor of the slot car according to different track conditions. Test results prove that the development of the autonomous driving system is successful.

A stable neural adaptive force controller for a hydraulic actuator

2003 ◽  
Vol 217 (4) ◽  
pp. 303-310 ◽  
Author(s):  
B Daachi ◽  
A Benallegue
Keyword(s):  
Neural Network ◽  
Hydraulic System ◽  
A Priori ◽  
Experimental Plant ◽  
Hydraulic Actuator ◽  
Adaptation Algorithm ◽  
Lyapunov Approach ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

In this paper a neural network adaptive force controller is proposed for a hydraulic system. The dynamic model of this system is highly non-linear and very complex to obtain. Thus, it is considered as a black box and a priori identification becomes necessary. A neural network is used to approximate the model and then a controller using the Lyapunov approach is designed. The neural network parameters are updated online according to an adaptation algorithm obtained via stability analysis. The performance of the proposed neural network controller is validated on an experimental plant.

Design of Control System for Detection of Sub-Nanometer Wood Flour Based on Neural Network

2011 ◽  
Vol 393-395 ◽  
pp. 44-48
Author(s):  
De Zhi Guo ◽  
Chun Mei Yang ◽  
Yan Ma
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Wood Flour ◽  
Network Control ◽  
Neural Network Control ◽  
Control Effect ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

In this paper, the detection of sub-nanometer wood flour based on neural network control, how to improved the quality of wood flour is proposed. In the analysis of the advantages of neural network controller, as the auxiliary controller for the PID controller, and improving the control effect of the system. With the contrast of the experimental results, illustrates the quality of the sub-nanometer wood flour has been improved by the neural network control.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Neuron Optimization Based PID Approach for Cutting Force Control

2006 ◽  
Vol 315-316 ◽  
pp. 85-89
Author(s):  
S. Jiang ◽  
Yan Shen Xu ◽  
J. Wu
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Open Loop ◽  
Network Control ◽  
Neural Network Control ◽  
System A ◽  
The Neural Network ◽  
Nc Milling ◽  
Hidden Layer ◽  
Control Principle

To improve the cutting efficiency, one of key approaches is to control with constant force in the full depth working condition. And the controller design is vital to realize the real-time feasibility and robustness of the system. A neuron optimization based PID approach is proposed in this paper and adopted in the NC cutting process. This approach optimizes the parameters of PID controller real-timely with the neural network control principle. It not only overcomes the mismatch of the open-loop system model which occurred in constant PID control, but also solves the contradiction between the calculation speed and precision in the neural network which caused by the node choosing of the hidden layer. At last, the simulation has been carried out on a NC milling machine to prove the validity and effectiveness of the proposed approach.

Application of BP Neural Network Controller on Inverted Pendulum

2013 ◽  
Vol 328 ◽  
pp. 72-76
Author(s):  
Huan Xin Cheng ◽  
Dao Sheng Zhang ◽  
Li Cheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Inverted Pendulum ◽  
Network Control ◽  
Coupled Systems ◽  
Control Effect ◽  
Strongly Coupled ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Mathematical Model

The traditional PID control, which is based on linearization, is often hard to obtain the optimal control effect on such nonlinear, multiple-output, strongly coupled systems like inverted pendulum. To solve the problem above, the BP neural network controller was developed for inverted pendulum. On the basis of establishing and analyzing the mathematical model of single inverted-pendulum, this paper established the state space expression, and then designed a neural network control system based on BP algorithm. The simulation was researched by Matlab and the running results show that this control has good robustness and can achieve satisfactory control effect.

A Neural Network Method for Miniature Unmanned Helicopter Heading Control

2012 ◽  
Vol 468-471 ◽  
pp. 93-96
Author(s):  
Meng Bai ◽  
Min Hua Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Network Control ◽  
Neural Network Control ◽  
Unmanned Helicopter ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Heading Control ◽  
Helicopter Aerodynamics

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

scholarly journals Application of Firefly Algorithm based Neural Network Controller

2019 ◽  
Vol 8 (11) ◽  
pp. 2659-2662
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Hidden Layer

The Firefly Algorithm is comparison of new optimize procedure based on PSO as tautness. The paper presents the competence and forcefulness of the Firefly algorithm as the optimize concept for a proportional–integral–derivative organizer under various loading conditions. The proposed PID controller is attempt to designed and implemented to frequency-control of a two area interconnected systems. The hidden layer formation is not personalized, as the interest lies only on the reckoning of the weights of the system. In sequence to obtain a practicable report, the weights of the neural network are computational or optimized by minimizing function cost or error. A Firefly Algorithm is an efficient but uncomplicated meta-heuristic optimization technique inspired by expected motion of fireflies towards more light, is used for the preparation of neural network. The simulation report view that the calculation competence of training progression using Firefly Optimization performance with Load frequency control. A study of the output report of the system PID controller and FA based neural network controllers are made for 1% change in load in area 1 and it is found that the proposed controllers ensures a better steady state response of the systems

scholarly journals A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3423 ◽  
Author(s):  
El Barhoumi ◽  
Ikram Ben Belgacem ◽  
Abla Khiareddine ◽  
Manaf Zghaibeh ◽  
Iskander Tlili
Keyword(s):  
Neural Network ◽  
Fuel Cell ◽  
Flow Rate ◽  
Fuel Flow Rate ◽  
Supply Pressure ◽  
Fuel Flow ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Interleaved Boost Converter

This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs.

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