Neural network based chattering free sliding mode control

2002 ◽  
Author(s):  
H. Morioka ◽  
K. Wada ◽  
A. Sabanovic ◽  
K. Jezernik
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Mode Control ◽  
Chattering Free

Chattering Free Sliding Mode Control with Observer Based Adaptive Radial Basis Function Neural Network for Temperature Tracking in a Fixed Bed Reactor

2019 ◽  
Vol 17 (10) ◽  
Author(s):  
Avdesh Singh Pundir ◽  
Kailash Singh
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Fixed Bed ◽  
High Gain ◽  
Fixed Bed Reactor ◽  
Mode Control ◽  
Chattering Free

Abstract In this paper, a Chattering Free Sliding Mode Control (CFSMC) with observer based adaptive Radial Basis Function Neural Network (RBFNN) has been designed for first-order transfer function model of temperature trajectory in a fixed bed reactor. The steady-state behavior and effect of different operating parameters such as feed velocity and temperature on the operation of the fixed bed reactor have been discussed. Due to RBFNN’s capability to map the nonlinear dynamics online through self-learning ability, it is combined with CFSMC to reduce the chattering behavior. The adaptive RBFNN has been used to approximate the nonlinear dynamic behavior of the fixed bed reactor. To predict the states of the system, high gain observer based on adaptive RBFNN has been used. Design parameter of the observer has been estimated using Hurwitz polynomial. The effect of neuron number on the mapping error and the effect of space discretization step on modeling error have also been discussed. To decrease the chattering generated by the Sliding Mode Controller (SMC) in the temperature trajectory tracking, an equivalent control term is neglected from the final controller. It has two main advantages: one is the reduction in chattering behavior which is the main drawback of SMC and the second is the reduction of the high gain requirement. The SMC is used to overcome against external disturbance, load variation, variation in key parameters and model mismatch. To make the simulation realistic, constraints have been applied to control input and input rate. For guaranteeing the system stability, Lyapunov theorem has been applied. To show the suitability of the hybrid controller, a comparison has been carried out between the hybrid and PID controller. To quantify the performance, Integral Time Weighted Absolute Error (ITAE) has been estimated. Under the condition of existing model errors and external disturbances, simulation study of the control of the fixed bed reactor shows that the hybrid control algorithm consisting of sliding mode control and observer-based adaptive RBFNN performs well both for tracking the temperature trajectory and reducing the chattering.

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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