Trajectory-Tracking-Based Adaptive Neural Network Sliding Mode Controller for Robot Manipulators

2020 ◽  
Vol 20 (3) ◽  
Author(s):  
Bin Ren ◽  
Yao Wang ◽  
Jiayu Chen
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Robot Dynamics ◽  
Sliding Mode Controller ◽  
Adaptive Neural Network ◽  
Proposed Model ◽  
The Impact

Abstract Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising method to understand robot dynamics with uncertainties and improve robot control. However, reliable trajectory-tracking-based controllers require high model precision and complexity. To develop an agile and straightforward method to mitigate the impact caused by uncertain disturbance and chattering, this study proposed an adaptive neural network sliding mode controller based on the super-twisting algorithm. The proposed model not only can minimize the tracking error but also improve the system robustness with a simpler structure. Moreover, the proposed controller has the following two distinctive features: (1) the weights of the radial basis function (RBF network) are designed to be adjusted in real-time and (2) the prior knowledge of the actual robot system is not required. The analytical model of the proposed controller was proved to be stable and ensured by the Lyapunov theory. To validate the proposed model, this study also conducted a comparative simulation on a two-link robot manipulator system with the conventional sliding mode controller and the model-based controller. The results suggest the proposed model improved the control accuracy and had fewer chattering.

scholarly journals A neural network combined with sliding mode controller for the two-wheel self-balancing robot

2021 ◽  
Vol 10 (3) ◽  
pp. 592
Author(s):  
Duc-Minh Nguyen ◽  
Van-Tiem Nguyen ◽  
Trong-Thang Nguyen
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Sliding Mode ◽  
Lyapunov Theory ◽  
Sliding Mode Controller ◽  
Control Laws ◽  
Noise Compensation ◽  
Simulation Results ◽  
Balancing Robot ◽  
The Stability

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

scholarly journals Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

2021 ◽  
Vol 10 (6) ◽  
pp. 120
Author(s):  
Monisha Pathak* ◽  
◽  
Dr. Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robotic Manipulator ◽  
Adaptive Sliding Mode Control ◽  
Control Approach ◽  
Adaptive Sliding Mode

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

Robust Hybrid Fractional Order Proportional Derivative Sliding Mode Controller for Robot Manipulator Based on Extended Grey Wolf Optimizer

Robotica ◽  
2019 ◽  
Vol 38 (4) ◽  
pp. 605-616 ◽  
Author(s):  
Hossein Komijani ◽  
Mojtaba Masoumnezhad ◽  
Morteza Mohammadi Zanjireh ◽  
Mahdi Mir
Keyword(s):  
Fractional Order ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Lyapunov Theory ◽  
Grey Wolf Optimizer ◽  
Sliding Mode Controller ◽  
Grey Wolf ◽  
Robust Controller ◽  
The Stability ◽  
Proportional Derivative Controller

SUMMARYThis paper presents a novel robust hybrid fractional order proportional derivative sliding mode controller (HFOPDSMC) for 2-degree of freedom (2-DOF) robot manipulator based on extended grey wolf optimizer (EGWO). Sliding mode controller (SMC) is remarkably robust against the uncertainties and external disturbances and shows the valuable properties of accuracy. In this paper, a new fractional order sliding surface (FOSS) is defined. Integrating the fractional order proportional derivative controller (FOPDC) and a new sliding mode controller (FOSMC), a novel robust controller based on HFOPDSMC is proposed. The bounded model uncertainties are considered in the dynamics of the robot, and then the robustness of the controller is verified. The Lyapunov theory is utilized in order to show the stability of the proposed controller. In this paper, the EGWO is developed by adding the emphasis coefficients to the typical grey wolf optimizer (GWO). The GWO and EGWO, then, are applied to optimize the proposed control parameters which result in the optimized GWO-HFOPDSMC and EGWO-HFOPDSMC, respectively. The effectivenesses of the optimized controllers (GWO-HFOPDSMC and EGWO-HFOPDSMC) are completely verified by comparing the simulation results of the optimized controllers with the typical FOSMC and HFOPDSMC.

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

2017 ◽  
Vol 22 (S3) ◽  
pp. 5799-5809 ◽  
Author(s):  
Fei Wang ◽  
Zhi-qiang Chao ◽  
Lian-bing Huang ◽  
Hua-ying Li ◽  
Chuan-qing Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Trajectory Tracking Control ◽  
Fuzzy Sliding Mode

Dynamic model estimating and designing controller for the 2-DoF planar robot in interaction with cable-driven robot based on adaptive neural network

2021 ◽  
pp. 1-20
Author(s):  
Vahid Bahrami ◽  
Ahmad Kalhor ◽  
Mehdi Tale Masouleh
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Reference Model ◽  
Sliding Mode ◽  
Dynamic Properties ◽  
Tracking Error ◽  
Main Idea ◽  
Robot Dynamics ◽  
Adaptive Neural Network ◽  
Planar Robot

This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively.

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