Multiple Models Adaptive Control Based on RBF Neural Network Dynamic Compensation

2005 ◽  
pp. 36-41 ◽  
Author(s):  
Junyong Zhai ◽  
Shumin Fei
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Rbf Neural Network ◽  
Multiple Models ◽  
Network Dynamic ◽  
Dynamic Compensation ◽  
Neural Network Dynamic

scholarly journals Adaptive neural network dynamic surface control for musculoskeletal robots

2014 ◽  
Author(s):  
Michael Jantsch ◽  
Steffen Wittmeier ◽  
Konstantinos Dalamagkidis ◽  
Guido Herrmann ◽  
Alois Knoll
Keyword(s):  
Neural Network ◽  
Dynamic Surface Control ◽  
Network Dynamic ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Surface Control ◽  
Neural Network Dynamic

scholarly journals RBF Neural Network Backstepping Sliding Mode Adaptive Control for Dynamic Pressure Cylinder Electrohydraulic Servo Pressure System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Pan Deng ◽  
Liangcai Zeng ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Sliding Mode ◽  
Model Simulation ◽  
Rbf Neural Network ◽  
Dynamic Pressure ◽  
Adaptive Controller ◽  
Pressure System ◽  
Output Pressure ◽  
Pressure Cylinder

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).

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
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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