Application of PID Neural Network Controller on Engine Throttle System

2012 ◽  
Vol 220-223 ◽  
pp. 939-942
Author(s):  
Huan Xin Cheng ◽  
Peng Li
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Network Algorithms ◽  
Learning Function ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Pid Neural Network ◽  
Self Learning ◽  
Controlled Object

A mathematical model of engine throttle as the controlled object is established and then the neural network algorithms and PID control are combined. With the self -learning function of the neural network, self -tunings of PID parameters are realized. The method overcomes disadvantages of PID as parameters which are difficult to determine and embodies better intelligence and robustness of the neural network, the simulation is researched by Matlab and the results show that the PID neural network controller is more accurate and adaptive than conventional PID.

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.

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.

Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

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 Implementation of Adaptive Neural Networks Controller for NXT SCARA Robot System

2017 ◽  
Vol 4 (1.) ◽  
Author(s):  
Wessam M. F. Abouzaid ◽  
Elsayed A. Sallam
Keyword(s):  
Neural Network ◽  
Dynamic Properties ◽  
Robot System ◽  
Neural Controller ◽  
Scara Robot ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Controller ◽  
Self Learning ◽  
Neural Network Controllers

Several neural network controllers for robotic manipulators have been developed during the last decades due to their capability to learn the dynamic properties and the improvements in the global stability of the system. In this paper, an adaptive neural controller has been designed with self learning to resolve the problems caused by using a classical controller. A comparison between the improved unsupervised adaptive neural network controller and the P controller for the NXT SCARA robot system is done, and the result shows the improvement of the self learning controller to track the determined trajectory of robotic automated controllers with uncertainties. Implementation and practical results were designed to guarantee online real-time.

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