scholarly journals Optimal Tuning of the SMC Parameters for a Two-Link Manipulator Co-Simulation Control

2021 ◽  
Author(s):  
Sinan Ilgen ◽  
Akif Durdu ◽  
Erdi Gulbahce ◽  
Abdullah Cakan
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Pid Controller ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Optimization Methods ◽  
Effective Control ◽  
Control Performance ◽  
Trajectory Tracking Control ◽  
Simplex Search

This paper presents the trajectory tracking control of a two-link planar robot manipulator using MSC Adams and MATLAB co-simulation which enables the innovative virtual prototyping of the systems without any mathematical expressions. Firstly, the tracking control performance of the planar manipulator is investigated using the Sliding Mode Control (SMC) controller and the Proportional Integral Derivative (PID) controller in terms of the performance analysis. As a result, the SMC demonstrates effective control performances compared to the PID controller according to the required trajectory, settling time, and end position of the system. Then, the SMC controller parameters are determined using the different optimization methods offered as open source by MATLAB/Response Optimization Toolbox and compared to each other. In the virtual co-simulation, the trajectory tracking control performance is observed to be improved by optimizing the parameters of the SMC controller using Simplex Search (SS) method. All control results are examined and presented with graphics and international error standards.

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.

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

Adaptive Sliding Mode Disturbance Observer Based Composite Trajectory Tracking Control for Robot Manipulator With Prescribed Performance

2021 ◽  
Author(s):  
Danni Shi ◽  
Jinhui Zhang ◽  
Zhongqi Sun ◽  
Yuanqing Xia
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Disturbance Observer ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Trajectory Tracking Control ◽  
Terminal Sliding Mode ◽  
Prescribed Performance ◽  
Adaptive Sliding Mode ◽  
Sliding Mode Disturbance Observer

Abstract In this paper, the problem of the composite trajectory tracking control for robot manipulator with lumped uncertainties including unmodeled dynamics and external disturbances is investigated. To achieve the active disturbance rejection, the adaptive sliding mode disturbance observer is proposed to estimate the unknown lumped uncertainties in the absence of the prior upper bound information on the lumped uncertainties. Then, by combining the non-singular terminal sliding mode control and prescribed performance control approaches, the composite trajectory tracking controller is designed, and not only the finite-time convergence of the trajectory tracking errors, but also the prescribed performances are guaranteed. Finally, by applying the proposed control scheme to a two-DOF manipulator system, the effectiveness and advantages are verified by numerical simulations.

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