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.

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.

scholarly journals A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1365
Author(s):  
Yuan Liu ◽  
Song Xu ◽  
Seiji Hashimoto ◽  
Takahiro Kawaguchi
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Reference Model ◽  
Mimo System ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Model Based ◽  
Neural Network Controller ◽  
Highly Nonlinear

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

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.

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

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.

Tail Deviation Mechanism and Neural Network Control of the Tandem Rolling Strap

2011 ◽  
Vol 103 ◽  
pp. 488-492
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yi Lun Liu
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Finite Element Model ◽  
Control Policy ◽  
Element Model ◽  
Rolling Process ◽  
Network Control ◽  
Neural Network Control ◽  
The Neural Network ◽  
The Finite Element Model

In the rolling process, deviation is the phenomenon that the strap width direction's centerline deviates from rolling system setting centerline,serious deviation will cause product quality drop and rolling equipment fault. This paper has established the finite element model to the hot tandem rolling aluminum strap, analyzed the strap’s deviation rule under four kinds of incentives,obtained the neural network predictive model and the control policy of the tail deviation.The result to analyze a set of fact deviation data shows this method may control tail deviation in preconcerted permission range.

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