Advanced Sliding Mode Online Training for Neural Network Flight Control Applications

2015 ◽  
Author(s):  
Philipp Schnetter ◽  
Jonas Kaste ◽  
Thomas Krüger
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Sliding Mode ◽  
Online Training ◽  
Control Applications

Control System Design of a Hex-Rotor Aircraft Based on the Neural Network Sliding Mode Method

2014 ◽  
Vol 971-973 ◽  
pp. 418-421 ◽  
Author(s):  
Chang Jun Zhao ◽  
Yue Bai ◽  
Xun Gong ◽  
Dong Fu Xu ◽  
Zhi Jun Xu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flight Control ◽  
Sliding Mode ◽  
Flight Performance ◽  
The Novel ◽  
Flight Control System ◽  
The Neural Network ◽  
Mode Method ◽  
Coupling Characteristics

For the existing Multi-rotor aircrafts, the under-actuation and strong coupling characteristics have a remarkable influence on their flight performance. In order to overcome this effect, a novel Hex-Rotor aircraft is proposed in this paper. Based on the unique configuration of its six driving rotors, the Hex-Rotor aircraft has the ability to achieve the real independent control on the space 6-DOF channels. An autonomous flight control system with neural network sliding mode is designed. The simulation proved that the novel Hex-Rotor aircraft has desired maneuvering capability,and thehe control system is able to guarantee the aircrafts tracking flight of the aircraft.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

scholarly journals Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 40076-40085
Author(s):  
Ngoc Phi Nguyen ◽  
Nguyen Xuan Mung ◽  
Ha Le Nhu Ngoc Thanh ◽  
Tuan Tu Huynh ◽  
Ngoc Tam Lam ◽  
...  
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Adaptive Sliding Mode ◽  
Mode Control

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

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