scholarly journals A Neural Network Based Sliding Mode Control for Tracking Performance with Parameters Variation of a 3-DOF Manipulator

2019 ◽  
Vol 9 (10) ◽  
pp. 2023 ◽  
Author(s):  
Hoai-Vu-Anh Truong ◽  
Duc-Thien Tran ◽  
Kyoung Kwan Ahn
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
External Disturbance ◽  
Robotic Manipulator ◽  
Tracking Performance ◽  
Mode Control ◽  
Tracking Motion ◽  
Three Degrees Of Freedom

The manipulator, in most cases, works in unstructured and changeable conditions. With large external variations, the demand for stability and robustness must be ensured. This paper proposes a neural network sliding mode control (NNSMC) to cope with uncertainties and improve the behavior of the robotic manipulator in the presence of an external disturbance. The proposed method is applied to the three degrees of freedom (DOF) manipulator. Some comparisons between the proposed and the conventional algorithms are given in both simulation and experiments to prove that the designed control can achieve higher accuracy in tracking motion.

Fuzzy Sliding Mode Control of Robotic Manipulators With Kinematic and Dynamic Uncertainties

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Haitao Liu ◽  
Tie Zhang
Keyword(s):  
Fuzzy Logic ◽  
Sliding Mode Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Robotic Manipulator ◽  
Mode Control ◽  
Control Scheme ◽  
Adaptive Fuzzy Logic ◽  
Fuzzy Sliding Mode ◽  
Dynamic Uncertainties

Sliding mode control is a very attractive control scheme with strong robustness to structured and unstructured uncertainties as well as to external disturbances. In this paper, a robust fuzzy sliding mode controller, which is combined with an adaptive fuzzy logic system, is proposed to improve the control performance of the robotic manipulator with kinematic and dynamic uncertainties. In this controller, the sliding mode control is employed to improve the control accuracy and the robustness of the robotic manipulator, and the fuzzy logic control is adopted to approximate various uncertainties and to eliminate the chattering without the help of any prior knowledge of system uncertainties. The effectiveness of the proposed controller is then verified by the simulations on a 2-DOF (degrees of freedom) robotic manipulator and the experiments on an SCARA robot with four degrees of freedom. Simulated and experimental results indicate that the proposed controller is effective in the robust tracking of the robotic manipulator with kinematic and dynamic uncertainties.

Neural Network Sliding Mode Control of 5DOFs Robotic Manipulator

2014 ◽  
Vol 898 ◽  
pp. 514-520 ◽  
Author(s):  
Chang Kai Xu ◽  
Ming Li ◽  
Jian Yin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Robotic Manipulator ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
The Stability

In this paper, a neural network sliding mode controller for a kind of 5DOFs robotic manipulator is proposed. A radial basis function (RBF) neural network is used as an estimator to approximate uncertainties of the system. The learning algorithm of the neural network improves the performance of the system. A globle terminal sliding mode control (GTSMC) is designed to guarantee the stability and improve the dynamic performance of the robotic manipulator. Simulation results show that the proposed NNSMC strategy is effective to ensure the robustness and dynamic performance of the 5DOFs robotic manipulator.

Trajectory-following of a 4WID-4WIS vehicle via feedforward–backstepping sliding-mode control

2021 ◽  
pp. 095440702110212
Author(s):  
Yu-long Lei ◽  
Guanzheng Wen ◽  
Yao Fu ◽  
Xingzhong Li ◽  
Boning Hou ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Tracking Performance ◽  
Luenberger Observer ◽  
Mode Control ◽  
Comprehensive Method ◽  
Backstepping Sliding Mode Control ◽  
Flexible Operation

Realisation of high-precision trajectory tracking is the key technology to achieve unmanned driving, which has an important impact on vehicle handling stability, safety and comfort. However, many confounding factors seriously restrict tracking performance and pose a great challenge to the design of the controller for tracking the desired trajectory. A comprehensive method that combines feedforward and backstepping sliding mode control is proposed for a four-wheel independent driving–four-wheel independent steering (4WID-4WIS) vehicle, which has the advantages of over-coupling, multiple degrees of freedom and flexible operation. The desired value is the target for the feedforward output of the controller, and the backstepping sliding mode control is used to overcome all kinds of disturbances. A mature and reliable Luenberger observer is designed to achieve good trajectory tracking performance for reducing some sensors. The proposed method is verified via MATLAB/Simulink simulation, which proved that the method has an excellent trajectory tracking performance.

scholarly journals Sliding Mode Control for 2 Degrees of Freedom Upper Limb Rehabilitation Robotic System under Uncertainties

2019 ◽  
Vol 9 (2) ◽  
pp. 3268-3274
Keyword(s):  
Sliding Mode Control ◽  
Upper Limb ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Robotic Manipulator ◽  
Upper Limb Rehabilitation ◽  
Mode Control ◽  
Rehabilitation Robots ◽  
High Frequency Oscillations ◽  
Limb Rehabilitation

Rehabilitation of patients suffering from post-stroke injuries via robots is now adapted word widely. The aim of this therapy is to restore and improve the dysfunction and the performance of the affected limbs doing repetitive tasks with the help of rehabilitation robots, as robots are best way to perform repetitive task without any monotony failure. Control of these rehabilitation robots is an important part to consider because of nonlinearity and uncertainty of the system. This paper presents nonlinear sliding mode controller (SMC) for controlling a 2 degrees of freedom (DOF) upper limb robotic manipulator. Sliding mode control is able to handle system uncertainties and parametric changes. One drawback of using SMC is high frequency oscillations called as chattering. This chattering can be reduced by using boundary layer technique. Experiments have been carried out under perturbed conditions and results have shown that SMC performs well and remain stable and thus proves to robust controller for upper limb robotic manipulator.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Mou Chen ◽  
Rong Mei ◽  
Bin Jiang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
External Disturbance ◽  
System Uncertainty ◽  
Nonlinear Disturbance Observer ◽  
Mode Control ◽  
Near Space ◽  
Nonlinear Disturbance

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.

Sliding-mode control of a three-degrees-of-freedom nanopositioner

2008 ◽  
Vol 10 (3) ◽  
pp. 267-276 ◽  
Author(s):  
Jing-Chung Shen ◽  
Wen-Yuh Jywe ◽  
Chien-Hung Liu ◽  
Yu-Te Jian ◽  
Jeffrey Yang
Keyword(s):  
Sliding Mode Control ◽  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Mode Control ◽  
Three Degrees Of Freedom

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.

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