Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system

2019 ◽  
Vol 330 ◽  
pp. 490-503 ◽  
Author(s):  
Xiaoyu Shi ◽  
Yuhua Cheng ◽  
Chun Yin ◽  
Xuegang Huang ◽  
Shou-ming Zhong
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Control Method ◽  
Rbf Neural Network ◽  
Dynamic Surface Control ◽  
Uncertain Nonlinear System ◽  
Adaptive Backstepping ◽  
Dynamic Surface ◽  
Surface Control

scholarly journals UAV adaptive trajectory tracking control under unknown time-varying disturbance and input saturation

2021 ◽  
Author(s):  
Yimin Zhou ◽  
Zengwu Tian
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Trajectory Tracking ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Dynamic Surface Control ◽  
Time Varying ◽  
Dynamic Surface ◽  
Surface Control

Abstract In this paper, the flight control of the Unmanned aerial vehicle (UAV) is discussed with the proposed adaptive dynamic surface control method owing to its underactuated and non-linear characteristics. The proposed control algorithm is based on radial basis function (RBF) neural network and anti-saturation auxiliary system to realize high-precision trajectory tracking under time-varying disturbances and input saturation. First, the nonlinear dynamic model of the UAV with disturbances is established with the aid of rigid body motion theory. With the adoption of the dynamic surface control algorithm, the error surface and the Lyapunov function are defined to design the preliminary control law of the designed controller. Then the RBF neural network is introduced to estimate and compensate the disturbance. Further, an anti - saturation module is designed to tackle the problem of input saturation. By using the Lyapunov stability theory, it is proved that the stability and signal consistency of the closed-loop system are bounded, along with the constrained conditions of the control parameters. Simulation experiments have been performed and the results demonstrate that the proposed control algorithm has high-precision trajectory tracking ability and strong anti-disturbance capability under the input saturation constraint with high control performance.

scholarly journals Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shaohua Luo
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Nonlinear Dynamic ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Dynamic Surface Control ◽  
Synchronous Motor ◽  
Unknown Parameters ◽  
Dynamic Surface ◽  
Surface Control

This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.

Adaptive neural network control for course-keeping of ships with input constraints

2017 ◽  
Vol 41 (4) ◽  
pp. 1010-1018 ◽  
Author(s):  
Qingling Wang ◽  
Changyin Sun ◽  
Yangyang Chen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Backstepping Technique ◽  
Input Constraints ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Controlled System ◽  
Surface Control ◽  
Adaptive Neural Network Control

In this paper, an adaptive neural network (NN) control method is proposed for the problem of nonlinear course control of ships with input constraints and unknown direction control gains. Specifically, dynamic surface control is used to overcome the problem of explosion of complexity inherent in the backstepping technique, and the Nussbaum function is employed to deal with the unknown signs of control gains. It is proved that the proposed adaptive NN control method, which is composed of dynamic surface control and a backstepping technique with the Nussbaum gain function, is able to guarantee uniform ultimate boundedness of all the signals in the controlled system. In addition, the tracking error between the output of the controlled system and a desired trajectory is shown to converge to a small neighbourhood of the origin. Finally, one example is introduced to illustrate the proposed theoretical results.

Sign in / Sign up

Export Citation Format

Share Document