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

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.141.303 ◽

2011 ◽

Vol 141 ◽

pp. 303-307 ◽

Cited By ~ 1

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.

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Design and implementation sliding mode controller based on radial basis function neural network for synchronous reluctance motor

2009 4th IEEE Conference on Industrial Electronics and Applications ◽

10.1109/iciea.2009.5138206 ◽

2009 ◽

Cited By ~ 1

Author(s):

Chien-An Chen ◽

Wen-Bin Lin ◽

Huann-Keng Chiang

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Design And Implementation ◽

Synchronous Reluctance Motor ◽

Reluctance Motor ◽

Radial Basis

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

International Journal of Control Automation and Systems ◽

10.1007/s12555-016-0650-1 ◽

2017 ◽

Vol 15 (6) ◽

pp. 2892-2905 ◽

Cited By ~ 17

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

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Synchronous reluctance motor speed drive using sliding mode controller based on Gaussian radial basis function neural network

Artificial Life and Robotics ◽

10.1007/s10015-009-0627-8 ◽

2009 ◽

Vol 14 (1) ◽

pp. 53-57 ◽

Cited By ~ 1

Author(s):

Chien-An Chen ◽

Huann-Keng Chiang ◽

Wen-Bin Lin ◽

Chih-Huang Tseng

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Motor Speed ◽

Synchronous Reluctance Motor ◽

Reluctance Motor ◽

Radial Basis

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

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220916484 ◽

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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Design and Implementation of a Sliding Mode Controller Using a Gaussian Radial Basis Function Neural Network Estimator for a Synchronous Reluctance Motor Speed Drive

Electric Power Components and Systems ◽

10.1080/15325008.2010.528542 ◽

2011 ◽

Vol 39 (6) ◽

pp. 548-562 ◽

Cited By ~ 12

Author(s):

Wen-Bin Lin ◽

Chien-An Chen ◽

Huann-Keng Chiang

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Motor Speed ◽

Design And Implementation ◽

Synchronous Reluctance Motor ◽

Reluctance Motor ◽

Radial Basis

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Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems

ISA Transactions ◽

10.1016/j.isatra.2018.11.021 ◽

2019 ◽

Vol 87 ◽

pp. 200-216 ◽

Cited By ~ 8

Author(s):

Nada M. Moawad ◽

Wael M. Elawady ◽

Amany M. Sarhan

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Sliding Mode Control ◽

Radial Basis Function ◽

Basis Function ◽

Sliding Mode ◽

Uncertain Nonlinear Systems ◽

Mode Control ◽

Radial Basis

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

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410019896430 ◽

2019 ◽

Vol 234 (6) ◽

pp. 1157-1171 ◽

Cited By ~ 1

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.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse8030210 ◽

2020 ◽

Vol 8 (3) ◽

pp. 210 ◽

Cited By ~ 3

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.

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