Feedback linearization and feedforward neural network control with application to twin rotor mechanism

2016 ◽  
Vol 40 (2) ◽  
pp. 351-362 ◽  
Author(s):  
Chia-Wei Lin ◽  
Tzuu-Hseng S Li ◽  
Chung-Cheng Chen
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Controller Design ◽  
Coupling Effect ◽  
Network Control ◽  
Feedforward Neural Network ◽  
Neural Network Control ◽  
Almost Disturbance Decoupling ◽  
Neural Network Controller ◽  
Twin Rotor

A twin rotor multi-input multi-output system (TRMMS) is a high-order nonlinear system with a significant cross-coupling effect. The control of TRMMSs is considered a markedly challenging topic in the field of robust control. This study proposes a novel feedback linearization and feedforward neural network controller design for a TRMMS with almost disturbance decoupling (ADD) capabilities. The proposed composite controller achieves exponentially global stability and ADD performance without applying any traditional parallel learning algorithms. This study proposes an organization of the feedforward neural network and the weights among the layers to guarantee the stability of the overall system. A number of nonlinear systems, which are too complex to be solved by general ADD studies, are proposed in this study to demonstrate that the proposed methodology can effectively achieve the tracking and ADD performances through Matlab. Moreover, an efficient algorithm is proposed for designing the feedback linearization and feedforward neural network control with ADD and tracking capabilities.

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.

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 High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

2019 ◽  
Vol 9 (17) ◽  
pp. 3472 ◽  
Author(s):  
Chen ◽  
Tao ◽  
Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Network Control ◽  
Parametric Estimation ◽  
Pneumatic Cylinder ◽  
Unmodeled Dynamics ◽  
Neural Network Control ◽  
Tracking Accuracy ◽  
Neural Network Controller

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

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.

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.

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