Neural network-based adaptive terminal sliding mode control for the deployment process of the dual-body tethered satellite system

2019 ◽  
Vol 234 (6) ◽  
pp. 1157-1171 ◽  
Author(s):  
Chenguang Liu ◽  
Wei Wang ◽  
Yong Guo ◽  
Shumin Chen ◽  
Aijun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
External Disturbance ◽  
Satellite System ◽  
Terminal Sliding Mode ◽  
Tethered Satellite ◽  
Tethered Satellite System ◽  
Radial Basis

The dual-body tethered satellite system, which consists of two spacecraft connected by a single tether, is one of the most promising configurations in numerous space missions. To ensure the stability of deployment, the radial basis function neural network-based adaptive terminal sliding mode controller is proposed for the dual-body tethered satellite system with the model uncertainty and external disturbance. The terminal sliding mode controller serves as the main control framework for its properties of the strong robustness and finite-time convergence. The radial basis function neural network is adopted to approximate the model uncertainty, in which the weight vector of the radial basis function neural networks and the unknown upper bound of the external disturbance are estimated by using two adaptive laws. Finally, the Lyapunov theory and numerical simulations are used to prove the validity of the proposed controller.

scholarly journals Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141982996 ◽  
Author(s):  
Lili Wan ◽  
Yixin Su ◽  
Huajun Zhang ◽  
Yongchuan Tang ◽  
Binghua Shi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Terminal Sliding Mode ◽  
Unmanned Surface Vehicle ◽  
The Neural Network ◽  
Fast Terminal Sliding Mode ◽  
Radial Basis ◽  
Unknown Disturbance

A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.

Global fast terminal sliding mode based radial basis function neural network for accurate fault estimation in nonlinear systems

2021 ◽  
pp. 1-17
Author(s):  
Wasif Shabbir ◽  
Li Aijun ◽  
Muhammad Taimoor ◽  
Cui Yuwei
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Real Time ◽  
Basis Function ◽  
Sliding Mode ◽  
Fault Estimation ◽  
Terminal Sliding Mode ◽  
Fast Terminal Sliding Mode ◽  
Radial Basis

The problem of quick and accurate fault estimation in nonlinear systems is addressed in this article by combining the technique of radial basis function neural network (RBFNN) and global fast terminal sliding mode control (GFTSMC) concept. A new strategy to update the neural network weights, by using the global fast terminal sliding surface instead of conventional error back propagation method, is introduced to achieve real time, quick and accurate fault estimation which is critical for fault tolerant control system design. The combination of online learning ability of RBFNN, to approximate any nonlinear function, and finite time convergence property of GFTSMC ensures quick detection and accurate estimation of faults in real time. The effectiveness of the proposed strategy is demonstrated through simulations using a nonlinear model of a commercial aircraft and considering a wide range of sensors and actuators faults. The simulation results show that the proposed method is capable of quick and accurate online fault estimation in nonlinear systems and shows improved performance as compared to conventional RBFNN and other techniques existing in literature.

scholarly journals Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

2020 ◽  
Vol 8 (3) ◽  
pp. 210 ◽  
Author(s):  
Renqiang Wang ◽  
Donglou Li ◽  
Keyin Miao
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Intelligent Control ◽  
Basis Function ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Unmanned Surface Vehicles ◽  
Radial Basis

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.

Fuzzy Sliding Mode Control for a Three-Links Spatial Robot Based on RBF Neural Network

2011 ◽  
Vol 141 ◽  
pp. 303-307 ◽  
Author(s):  
Sheng Bin Hu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Sliding Mode Controller ◽  
System A ◽  
Radial Basis ◽  
Fuzzy Sliding Mode

To achieve the tracing control of a three-links spatial robot, a adaptive fuzzy sliding mode controller based on radial basis function neural network is proposed in this paper. The exponential sliding mode controller is divided into two parts: equivalent part and exponential corrective part. To realize the control without the model information of the system, a radial basis function neural network is designed to estimate the equivalent part. To diminish the chattering, a fuzzy controller is designed to adjust the corrective part according to sliding surface. The simulation studies have been carried out to show the tracking performance of a three-links spatial robot. Simulation results show the validity of the control scheme.

scholarly journals Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094756
Author(s):  
Dong-hui Wang ◽  
Shi-jie Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Tracking Error ◽  
Adaptive Tracking ◽  
Uncertain Dynamics ◽  
Radial Basis

In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

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