Application of RBF Neural Network in the Manipulator Trajectory Planning

2014 ◽  
Vol 651-653 ◽  
pp. 659-662 ◽  
Author(s):  
Meng Meng Du ◽  
Hai Tao Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Control Strategy ◽  
Polynomial Interpolation ◽  
Rbf Neural Network ◽  
Joint Space ◽  
Interpolation Algorithm ◽  
Matlab Simulation ◽  
Nonlinear Characteristics ◽  
Target Trajectory

In view of the high order, nonlinear characteristics of target trajectory, control strategy is proposed for a kind of RBF neural network and polynomial interpolation combining. According to the specific requirements of trajectory planning, the manipulator in joint space variables are expressed as one or two order derivative function by polynomial interpolation algorithm, then the RBF neural network is used for tracking the target trajectory. Finally, through MATLAB simulation, the accuracy of this method is verified.

The Kinematic Analysis and Trajectory Planning Study of High-Speed SCARA Robot Handling Operation

2014 ◽  
Vol 687-691 ◽  
pp. 294-299 ◽  
Author(s):  
Guo Qing Ma ◽  
Zheng Lin Yu ◽  
Guo Hua Cao ◽  
Yan Bin Zheng ◽  
Li Liu
Keyword(s):  
Trajectory Planning ◽  
Inverse Kinematics ◽  
High Speed ◽  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Joint Space ◽  
Parameter Design ◽  
Matlab Simulation ◽  
Scara Robot ◽  
Cubic Polynomial

Successfully developed of high-speed SCARA robot provides the possibility for fast handling. After analyzed the mechanical structure of SCARA robot, the kinematics equations were built to analyze forward and inverse kinematics problems based on modified D-H coordinate system theory. The trajectory planning was achieved by using the cubic polynomial interpolation method in joint space over the path points combined with motion parameters, the kinematics and trajectory planning were simulated by using matlab simulation platform. Simulation results show that robot parameter design is reasonable and the trajectory planning by interpolation calculation in joint space is feasible.

scholarly journals Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1207
Author(s):  
Qisong Song ◽  
Shaobo Li ◽  
Qiang Bai ◽  
Jing Yang ◽  
Ansi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Polynomial Interpolation ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Degree Of Freedom ◽  
Quintic Polynomial ◽  
High Degree

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

Study on RBF Neural Network PID Control for Hydro-Viscous Drive System

2013 ◽  
Vol 470 ◽  
pp. 668-672
Author(s):  
Qing Rui Meng ◽  
Kai Wang ◽  
Dao Ming Wang ◽  
Jian Wang ◽  
Bao Cheng Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Control Strategy ◽  
Pid Control ◽  
Rbf Neural Network ◽  
Drive System ◽  
Output Curve ◽  
Strategy Selection ◽  
Neural Network Pid ◽  
Selection Of

To verify the applicability of RBF neural network PID control on speed regulating start control for hydro-viscous drive system, analyze the principle of RBF neural network PID control, the simulation model is established based on SIMULINK and the control characteristics are analyzed based on the AMESim/MATLAB co-simulation. The results show that RBF neural network PID control has a good self-correcting effect on speed regulating start of hydro-viscous; it can make right judgments according to the error and error rate and adjust the output speed towards opposite direction of error; meanwhile, it ensures the smoothness of output curve and avoids excessive mechanical impact. The results play a guiding role for control strategy selection of speed regulating start.

scholarly journals Model Free Adaptive Control of Switched Reluctance Motor for Electric Vehicle

2021 ◽  
pp. 104-114
Author(s):  
Xifeng Mi , Yuanyuan Fan
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Control Method ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Torque Distribution ◽  
Switched Reluctance ◽  
Model Free ◽  
Reluctance Motor

In this paper, the model free adaptive control method of switched reluctance motor for electric vehicle is studied. Based on the torque distribution control of SRM, a SRM control strategy based on torque current hybrid model based on RBF neural network is proposed in this paper. Based on the deviation between the dynamic average value and instantaneous value of SRM output torque, the online learning of RBF neural network is realized. At the same time, this paper constructs a torque current hybrid model, obtains the current variation law of SRM under low torque ripple operation, and reduces the torque ripple of SRM. The SRM torque distribution control is realized on the SRM experimental platform. Compared with the voltage chopper control method, the experimental results show that the torque ripple of SRM can be reduced by adopting the torque distribution control strategy.

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