Intelligent controller for hybrid force and position control of robot manipulators using RBF neural network

2018 ◽  
Vol 7 (2) ◽  
pp. 767-775 ◽  
Author(s):  
Komal Rani ◽  
Naveen Kumar
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Robot Manipulators ◽  
Rbf Neural Network ◽  
Intelligent Controller

scholarly journals Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jie Zhao ◽  
Jun Zhong ◽  
Jizhuang Fan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Phenomenological Model ◽  
Pid Controller ◽  
Position Control ◽  
Rbf Neural Network ◽  
Pneumatic Muscle ◽  
Highly Nonlinear ◽  
Experimental Apparatus ◽  
Pneumatic Muscle Actuator

Pneumatic Muscle Actuator (PMA) has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

Position control for permanent magnet synchronous motor based on neural network and terminal sliding mode control

2020 ◽  
Vol 42 (9) ◽  
pp. 1632-1640
Author(s):  
Wenwu Zhu ◽  
Dongbo Chen ◽  
Haibo Du ◽  
Xiangyu Wang
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Finite Time ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Terminal Sliding Mode

A finite-time control strategy is proposed to solve the problem of position tracking control for a permanent magnet synchronous motor servo system. It can guarantee that the motor’s desired position can be tracked in a finite time. Firstly, for the d-axis voltage, a first-order finite-time controller is designed based on the vector control principle. Then, for the q-axis voltage, based on a radial basis function (RBF) neural network, an integral high-order terminal sliding mode controller is designed. Theoretical analysis shows that under the proposed controller, the desired position can be tracked by the motor position in a finite time. Simulation results are given to show that the proposed control method has a strong anti-disturbance ability and a fast convergence performance.

scholarly journals Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Shuhuan Wen ◽  
Junying Yuan ◽  
Jinghai Zhu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Robotic Manipulation ◽  
System Stability ◽  
External Interference ◽  
Functional Link ◽  
Rbf Network ◽  
Radial Basis

This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF) neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN) is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.

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