A control scheme based on RBF Neural Network for High-precision Servo System

2010 ◽  
Author(s):  
Hu Hongjie ◽  
Yue Jinyu ◽  
Zhan Ping
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Rbf Neural Network ◽  
Servo System ◽  
Control Scheme

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.

scholarly journals Contour control of biaxial motion system based on RBF neural network and disturbance observer

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110348
Author(s):  
Sanxiu Wang ◽  
Shengtao Jiang
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Disturbance Observer ◽  
Control Method ◽  
Rbf Neural Network ◽  
Servo System ◽  
Observation Error ◽  
Tracking Accuracy ◽  
Motion System ◽  
Contour Control

Friction is the main factor which degrades the control precisions of the servo system. In this paper, a cross coupled control method based on RBF neural network and disturbance observer is proposed for multi-axis servo system with LuGre friction, in order to implement high precision tracking and contouring control. Firstly, a feedback linearization controller is designed to realize the position stable tracking for single-axis motion; then, the disturbance observer is used to observe and compensate the friction. However, in practical application, the observation gain is difficult to select, and it is easy to cause observation error. In order to enhance the tracking accuracy and system robustness, the RBF neural network is introduced to approximate the disturbance observation error online. Finally, the cross coupled control is used to coordinate the motion between the axes to improve the contour accuracy. The simulation results show that the proposed method can effectively compensate the influence of friction on the system, has good tracking accuracy and high contour control precision.

Servo Control System of NC Modifying Forming Grinding Wheel Based on Cross Coupling Theory

2013 ◽  
Vol 823 ◽  
pp. 80-83
Author(s):  
Hong Lu ◽  
Shi Tong Xie ◽  
You Wang
Keyword(s):  
High Precision ◽  
Dynamic Performance ◽  
External Disturbance ◽  
Servo System ◽  
Cross Coupling ◽  
Coupled System ◽  
Synchronization Control ◽  
Grinding Wheel ◽  
Control Scheme ◽  
Cross Coupling Control

This paper studies the synchronous control strategy which can meet certain dynamic performance and the closed loop robustness to external disturbance in servo system. This servo system has two same PMSMs which are used in a new CNC molding machine for forming grinding wheel. To achieve high-precision of the machine, we designed the hardware construction of the servo system. At the same time, this paper proposes a synchronization control scheme based on cross coupling control algorithms. The performance of the cross-coupled system is theoretically analyzed and simulated. It was proved that this synchronization control scheme has quick response, robustness and small dynamic process synchronization error, which could meet requirements of the high-precision synchronization control.

Iterative learning radial basis function neural networks control for unknown multi input multi output nonlinear systems with unknown control direction

2019 ◽  
Vol 41 (12) ◽  
pp. 3452-3467 ◽  
Author(s):  
Tarek Bensidhoum ◽  
Farah Bouakrif ◽  
Michel Zasadzinski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Iterative Learning ◽  
Unknown Control Directions ◽  
Control Scheme ◽  
Unknown Control

In this paper, an iterative learning radial basis function neural-networks (RBF NN) control algorithm is developed for a class of unknown multi input multi output (MIMO) nonlinear systems with unknown control directions. The proposed control scheme is very simple in the sense that we use just a P-type iterative learning control (ILC) updating law in which an RBF neural network term is added to approximate the unknown nonlinear function, and an adaptive law for the weights of RBF neural network is proposed. We chose the RBF NN because it has universal approximation capabilities and can approximate any continuous function. In addition, among the advantages of our controller scheme is the fact that it is applicable to deal with a class of nonlinear systems without the need to satisfy the global Lipschitz continuity condition and we assume, only, that the unstructured uncertainty is norm-bounded by an unknown function. Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. Thus, the Nussbaum-type function is used to solve the problem of unknown control directions. In order to prove the asymptotic stability of the closed-loop system, a Lyapunov-like positive definite sequence is used, which is shown to be monotonically decreasing under the control design scheme. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

Analysis of Speed Servo System of Pneumatic Manipulator Based RBF Neural Network PID Control

2010 ◽  
Vol 40-41 ◽  
pp. 65-70 ◽  
Author(s):  
Jing Luo ◽  
Rui Bo Yuan ◽  
Yu Bi Yuan ◽  
Shao Nan Ba ◽  
Zong Cheng Zhang
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Hybrid Control ◽  
Rbf Neural Network ◽  
Servo System ◽  
Fast Response ◽  
Quick Response ◽  
Pneumatic Servo System ◽  
The Stability ◽  
Neural Network Pid

Through analysis and comparison of simple PID control and RBF neural network-PID hybrid control of the pneumatic servo system, then compared the stability and quick response under the two control system. Concluded that RBF neural network-PID hybrid control has better stability and fast response than the simple PID control.

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