Adaptive Control for Robotic Manipulators base on RBF Neural Network

According to the hydraulic principle diagram of the subgrade test device, the dynamic pressure cylinder electrohydraulic servo pressure system math model and AMESim simulation model are established. The system is divided into two parts of the dynamic pressure cylinder displacement subsystem and the dynamic pressure cylinder output pressure subsystem. On this basis, a RBF neural network backstepping sliding mode adaptive control algorithm is designed: using the double sliding mode structure, the two RBF neural networks are used to approximate the uncertainties in the two subsystems, provide design methods of RBF sliding mode adaptive controller of the dynamic pressure cylinder displacement subsystem and RBF backstepping sliding mode adaptive controller of the dynamic pressure cylinder output pressure subsystem, and give the two RBF neural network weight vector adaptive laws, and the stability of the algorithm is proved. Finally, the algorithm is applied to the dynamic pressure cylinder electrohydraulic servo pressure system AMESim model; simulation results show that this algorithm can not only effectively estimate the system uncertainties, but also achieve accurate tracking of the target variables and have a simpler structure, better control performance, and better robust performance than the backstepping sliding mode adaptive control (BSAC).

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Research on manipulator trajectory tracking with model approximation RBF neural network adaptive control

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978199 ◽

2017 ◽

Cited By ~ 3

Author(s):

Jing Jiang ◽

Linlin Pan ◽

Ying Dai ◽

Long Che

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Trajectory Tracking ◽

Rbf Neural Network ◽

Model Approximation

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Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

Mathematical Problems in Engineering ◽

10.1155/2019/6478506 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Yuqi Wang ◽

Qi Lin ◽

Xiaoguang Wang ◽

Fangui Zhou

Keyword(s):

Neural Network ◽

Adaptive Control ◽

Control Method ◽

Rbf Neural Network ◽

Approximation Error ◽

Wind Tunnel Test ◽

Parallel Robot ◽

Network Control ◽

Pd Control ◽

Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

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Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽

10.1155/2020/3172431 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Jiazhi Li ◽

Weicun Zhang ◽

Quanmin Zhu

Keyword(s):

Neural Network ◽

Adaptive Control ◽

External Disturbance ◽

Lyapunov Stability Theory ◽

Robotic Manipulators ◽

Adaptive Observer ◽

Multiple Model ◽

Neural Network Controller ◽

The Neural Network ◽

The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

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