RBF Neural Network Adaptive Robust Sliding Mode Control Method of Artillery Ammunition Transfer Arm

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.

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A Control Method for Nonlinear Systems Using Combined Sliding Mode Control and RBF Neural Network

International Journal of Electrical and Electronics Engineering ◽

10.14445/23488379/ijeee-v5i6p103 ◽

2018 ◽

Vol 5 (6) ◽

pp. 11-15

Author(s):

Ngoc Trung Dang ◽

◽

Huyen Linh Le Thi

Keyword(s):

Neural Network ◽

Nonlinear Systems ◽

Sliding Mode Control ◽

Control Method ◽

Sliding Mode ◽

Rbf Neural Network ◽

Mode Control

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Neural Network Based Sliding Mode Control for DC-DC Converters

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.211-212.395 ◽

2011 ◽

Vol 211-212 ◽

pp. 395-399

Author(s):

Li Ping Fan ◽

Ya Zhou Yu

Keyword(s):

Neural Network ◽

Sliding Mode Control ◽

Control Method ◽

Sliding Mode ◽

Rbf Neural Network ◽

Control Input ◽

Output Layer ◽

Mode Control ◽

Layer Function ◽

Network Output

DC-DC converters have some inherent characteristics such as high nonlinearity and time-variation, which often result in some difficulties in designing control schemes. RBF neural network sliding-mode control method is applied to PWM-based DC-DC converters in this paper. As the control input is duty cycle, the control inputs are in the scopes between 0 and 1. A general RBF neural network can not be suitable to control the PWM-based DC-DC converters, because the output layer of such network uses a linear function, and that the outputs of the network are between -∞ and +∞. Sigmoidal function is used instead of the output layer function in this paper to make the outputs are between 0 and 1. This sliding mode control method based on neural network can not only control the scope of the network output, but also eliminate the system chattering. Simulation experiments verify that this method can control the DC-DC converters well.

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

Recent Patents on Mechanical Engineering ◽

10.2174/2212797613999200730211514 ◽

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.

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