Robustness and fault-tolerance of cubic neural network intelligent control method - comparison with sliding mode control

2004 ◽  
Author(s):  
M. Takahashi ◽  
T. Narukawa ◽  
K. Yoshida
Keyword(s):  
Neural Network ◽  
Fault Tolerance ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Method ◽  
Sliding Mode ◽  
Method Comparison ◽  
Mode Control

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 A Modified RBF Neuro-Sliding Mode Control Technique for a Grid Connected PMSG Based Variable Speed Wind Energy Conversion System

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Rostand Marc Douanla ◽  
Godpromesse Kenné ◽  
François Béceau Pelap ◽  
Armel Simo Fotso
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Control Technique ◽  
Nonlinear Controller ◽  
Parameter Uncertainties ◽  
Power Extraction ◽  
Mode Control ◽  
The Neural Network

A modified control scheme based on the combination of online trained neural network and sliding mode techniques is proposed to enhance maximum power extraction for a grid connected permanent magnet synchronous generator (PMSG) wind turbine system. The proposed control method does not need the knowledge of the uncertainty bounds nor the exact model of the nonlinear system. Since the neural network is trained online, the time to estimate good weights can affect the dynamic performance of the process during the startup phase. Therefore an appropriate way to smoothly and explicitly accelerate the neural network rate of convergence during the startup phase is proposed. Furthermore, a flexible grid side voltage source converter control structure which can handle both grid connected and standalone modes based on conventional proportional integral (PI) control method is presented. Simulations are done in Matlab/Simulink environment to verify the effectiveness and assess the performance of the proposed controller. The results analysis shows the superiority of the proposed RBF neuro-sliding mode controller compared to a nonlinear controller based on sliding mode control method when the system undergoes parameter uncertainties.

Synchronization of Chaotic Gyro Systems via Sliding Mode Control with Cooperative Weights Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 1101-1104
Author(s):  
Yan Qiu Che ◽  
Ting Ting Yang ◽  
Xiao Qin Li ◽  
Rui Xue Li
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Closed Loop ◽  
Control Method ◽  
Sliding Mode ◽  
Closed Loop System ◽  
External Disturbances ◽  
Mode Control ◽  
Nonlinear Uncertainties ◽  
Stability Method

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

scholarly journals Path following of ship based on sliding mode control with improved RBF neural network and virtual circle

2021 ◽  
Vol 39 (1) ◽  
pp. 216-223
Author(s):  
Zongxuan Li ◽  
Renxiang Bu ◽  
Hugan Zhang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Path Following ◽  
Nonlinear Observer ◽  
Measured Velocity ◽  
Mode Control ◽  
Tangent Function

To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness.

Sign in / Sign up

Export Citation Format

Share Document