Radial basis function neural network based adaptive fast nonsingular terminal sliding mode controller for piezo positioning stage

2017 ◽  
Vol 15 (6) ◽  
pp. 2892-2905 ◽  
Author(s):  
To Xuan Dinh ◽  
Kyoung Kwan Ahn
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Terminal Sliding Mode ◽  
Nonsingular Terminal Sliding Mode ◽  
Positioning Stage ◽  
Radial Basis

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.

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 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.

Design of Vehicle Stability Controller Based on Fuzzy Radial Basis Neural Network Sliding Mode Theory with Sideslip Angle Estimation

2021 ◽  
Vol 11 (3) ◽  
pp. 1231
Author(s):  
Zhenzhao Zhang ◽  
Liang Chu ◽  
Jiaxu Zhang ◽  
Chong Guo ◽  
Jing Li
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
High Precision ◽  
Basis Function ◽  
Sliding Mode ◽  
Stability And Convergence ◽  
Vehicle Stability ◽  
Sliding Mode Controller ◽  
Sideslip Angle ◽  
Radial Basis

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.

Dual feedforward neural networks based synchronized sliding mode controller for cooperative manipulator system under variable load and uncertainties

2020 ◽  
Vol 234 (19) ◽  
pp. 3859-3872
Author(s):  
Jin Wang ◽  
Anbang Zhai ◽  
Fan Xu ◽  
Haiyun Zhang ◽  
Guodong Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Feedforward Neural Networks ◽  
Internal Force ◽  
Variable Load ◽  
Sliding Mode Controller ◽  
Radial Basis

The problem of simultaneous position and internal force control is discussed with cooperative manipulators system under variable load and dynamic uncertainties in this study. A position synchronized sliding mode controller is proposed in the presence of variable load, as well as modeling uncertainties, joint friction, and external disturbances. To deal with the complex situation brought by variable load, virtual synchronization coupled errors are introduced for internal force tracking control and joint synchronization in the meantime. Dual feedforward neural networks are adopted, where a radial basis function-neural network based dynamic compensator and a radial basis function-neural network based internal force estimator are established, respectively, so that precise dynamic knowledge and force measurement are out of demand through their cooperation. Together with simulation studies and analysis, the position and internal force errors are shown to converge asymptotically to zero. Using Lyapunov stability approach, the proposed controller is proven to be robust in face of variable external load and the aforementioned uncertainties.

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