Sliding Mode Control for a Class of Uncertain MIMO Nonlinear Systems with Application to Near-Space Vehicles

We propose a robust sliding mode control (SMC) scheme for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with the unknown external disturbance, the system uncertainty, and the backlash-like hysteresis. To tackle the continuous system uncertainty, the radial basis function (RBF) neural network is employed to approximate it. And then, combine the unknown external disturbance, and the unknown neural network approximation error with the affection caused by backlash-like hysteresis as a compounded disturbance which is estimated using the developed nonlinear disturbance observer. The robust sliding mode control based on the nonlinear disturbance observer and RBF neural network is presented to track the desired system output in the presence of the unknown system uncertainty, the external disturbance, and the backlash-like hysteresis. Finally, the designed robust sliding mode control strategy is applied to the near-space vehicle (NSV) attitude dynamics, and simulation results are given to illustrate the effectiveness of the proposed sliding mode control approach.

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Sliding mode control of the automobile electro-coating conveying mechanism with a nonlinear disturbance observer

Advances in Mechanical Engineering ◽

10.1177/1687814018795748 ◽

2018 ◽

Vol 10 (9) ◽

pp. 168781401879574 ◽

Cited By ~ 4

Author(s):

Wei Yuan ◽

Guoqin Gao

Keyword(s):

Sliding Mode Control ◽

Disturbance Observer ◽

High Performance ◽

Sliding Mode ◽

External Disturbance ◽

Nonlinear Disturbance Observer ◽

Control Approach ◽

Mode Control ◽

Highly Nonlinear ◽

Nonlinear Disturbance

The trajectory-tracking performance of the automobile electro-coating conveying mechanism is severely interrupted by highly nonlinear crossing couplings, unmodeled dynamics, parameter variation, friction, and unknown external disturbance. In this article, a sliding mode control with a nonlinear disturbance observer is proposed for high-accuracy motion control of the conveying mechanism. The nonlinear disturbance observer is designed to estimate not only the internal/external disturbance but also the model uncertainties. Based on the output of the nonlinear disturbance observer, a sliding mode control approach is designed for the hybrid series–parallel mechanism. Then, the stability of the closed-loop system is proved by means of a Lyapunov analysis. Finally, simulations with typical desired trajectory are presented to demonstrate the high performance of the proposed composite control scheme.

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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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