scholarly journals An Improved Fuzzy Neural Network Compound Control Scheme for Inertially Stabilized Platform for Aerial Remote Sensing Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiangyang Zhou ◽  
Yating Li ◽  
Yuan Jia ◽  
Libo Zhao
Keyword(s):  
Disturbance Rejection ◽  
Fuzzy Neural Network ◽  
Variable Universe ◽  
Compound Control ◽  
Sensing Applications ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Remote Sensing Applications ◽  
Stabilized Platform ◽  
Inertially Stabilized Platform

An improved fuzzy neural network (FNN)/proportion integration differentiation (PID) compound control scheme based on variable universe and back-propagation (BP) algorithms is proposed to improve the ability of disturbance rejection of a three-axis inertially stabilized platform (ISP) for aerial remote sensing applications. In the design of improved FNN/PID compound controller, the variable universe method is firstly used for the design of the fuzzy/PID compound controller; then, the BP algorithm is utilized to finely tune the controller parameters online. In this way, the desired performances with good ability of disturbance rejection and high stabilization accuracy are obtained for the aerial ISP. The simulations and experiments are, respectively, carried out to validate the improved FNN/PID compound control method. The results show that the improved FNN/PID compound control scheme has the excellent capability in disturbance rejection, by which the ISP’s stabilization accuracy under dynamic disturbance is improved significantly.

scholarly journals A High-Precision Control Scheme Based on Active Disturbance Rejection Control for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiangyang Zhou ◽  
Chao Yang ◽  
Beilei Zhao ◽  
Libo Zhao ◽  
Zhuangsheng Zhu
Keyword(s):  
Disturbance Rejection ◽  
Precision Control ◽  
Sensing Applications ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Control Scheme ◽  
Remote Sensing Applications ◽  
Aerial Remote Sensing ◽  
Stabilized Platform ◽  
Inertially Stabilized Platform

This paper presents a high-precision control scheme based on active disturbance rejection control (ADRC) to improve the stabilization accuracy of an inertially stabilized platform (ISP) for aerial remote sensing applications. The ADRC controller is designed to suppress the effects of the disturbance on the stabilization accuracy that consists of a tracking differentiator, a nonlinear state error feedback, and an extended state observer. By the ADRC controller, the effects of both the internal uncertain dynamics and the external multisource disturbances on the system output are compensated as a total disturbance in real time. The disturbance rejection ability of the ADRC is analyzed by simulations. To verify the method, the experiments are conducted. The results show that compared with the conventional PID controller, the ADRC has excellent capability in disturbance rejection, by which the effect of main friction disturbance on the control system can be weakened seriously and the stabilization accuracy of the ISP is improved significantly.

scholarly journals Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Xiangyang Zhou ◽  
Hao Gao ◽  
Yuan Jia ◽  
Lingling Li ◽  
Libo Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Parameter Optimization ◽  
Fuzzy Neural Network ◽  
Back Propagation ◽  
Optimization Method ◽  
Sensing Applications ◽  
Fuzzy Neural ◽  
Composite Parameter ◽  
Stabilized Platform ◽  
Neural Network Pid

This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

Temperature Control of Batch Reaction Based on Genetic Fuzzy Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1968-1971 ◽  
Author(s):  
Chun Tao Man ◽  
Jia Cui ◽  
Xin Xin Yang ◽  
Jun Kai Wang ◽  
Tian Feng Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Design Method ◽  
Batch Reactor ◽  
Batch Processes ◽  
Strong Nonlinearity ◽  
Control Scheme ◽  
Fuzzy Neural

The batch reactor has strong nonlinearity and hysteresis, the conventional control method is hard to meet the control requirements. According to the batch processes temperature control, this thesis proposed an intelligent control scheme. Combined neural networks with fuzzy logic control, searching and optimized parameters of fuzzy neural network by using Genetic Algorithm (GA), displayed the design method and optimization steps, and the simulation results verify the control scheme which proposed is feasible and effective.

scholarly journals Variable steering ratio design and handling stability research for steer-by-wire forklift

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882289 ◽  
Author(s):  
Junjie Huang ◽  
Benxian Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Lateral Acceleration ◽  
Ratio Method ◽  
Dynamic Variable ◽  
Advantages And Disadvantages ◽  
Control Scheme ◽  
Steer By Wire ◽  
Fuzzy Neural ◽  
Verification Model

For the TFC35 steer-by-wire forklift, a linear 3-degree-of-freedom forklift model is given, which provides a verification model for variable steering ratio design. The concept of ideal steering ratio is expounded. The variable steering ratio algorithm based on constant yaw rate gain and the variable steering ratio algorithm based on constant lateral acceleration gain are studied. A variable steering ratio algorithm based on two kinds of gain combination is proposed. The advantages and disadvantages of the above three static variable steering ratio algorithms based on constant gain are simulated and analyzed. Aiming at complex working conditions, a dynamic variable steering ratio control scheme based on fuzzy neural network is proposed. The definition, implementation steps, and adjustment algorithms of each layer of fuzzy neural network are given. The final experiment results show that the dynamic variable steering ratio method based on fuzzy neural network is more resistant to the disturbance of its own parameters and can be used in complex dynamic conditions, which helps to improve the handling stability of the forklift.

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