A self-adaptive region fuzzy guidance law based on RBF neural network for attacking UAV

Author(s):  
Jinyong Yu ◽  
Qingjiu Xu ◽  
Yue Zhi
2016 ◽  
Vol 10 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Liang Feng

Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower cost,a new Taylor-series expansion location algorithm based on Self-adaptive RBF neural network (SA-RBF-TSE) is proposed to estimate the initial value. The proposed algorithm is analysed and simulated with several other algorithms in this paper.


2022 ◽  
pp. 1-20
Author(s):  
G. Wu ◽  
K. Zhang ◽  
Z. Han

Abstract In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.


2015 ◽  
Vol 764-765 ◽  
pp. 718-723
Author(s):  
Xin Li ◽  
Chen Lu ◽  
Zi Li Wang

A rotary actuator that employs hydraulic oil as the power source has a direct rotary structure. It is an important structure and has been widely utilized in aircrafts and ships because of its advantages, including large torque/quality ratio, simple compact structure, and fast dynamic response. Huge damage may be caused when a rotary actuator breaks down during operation. However, only a few studies have focused on fault detection and performance assessment for rotary actuators. In this study, a method that detects the fault in and assesses the performance of the rotary actuator based on residual analysis is proposed. The data in normal state are utilized to build an observer with two radial basis function (RBF) neural networks. One RBF neural network is employed to estimate the expected output required to generate the residuals. The self-adaptive thresholds are obtained through the other RBF neural network. The residual is then inputted into the self-organizing mapping neural network trained by the residual values in normal state to normalize the performance of the rotary actuator into confidences values between 0 and 1. Finally, the detection and assessment of two typical faults of the rotary actuator are simulated. Results verify the efficiency of the proposed method.


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