scholarly journals Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

2020 ◽  
Vol 10 (1) ◽  
pp. 396-407
Author(s):  
Fatiha Loucif ◽  
Sihem Kechida
Keyword(s):  
Sliding Mode ◽  
Robot Manipulator ◽  
Absolute Error ◽  
Grey Wolf Optimizer ◽  
Optimal Parameters ◽  
Error Criterion ◽  
Trajectory Tracking Control ◽  
Integral Time ◽  
Sine Cosine Algorithm ◽  
Antlion Optimization

AbstractIn this paper, a sliding mode controller (SMC) with PID surface is designed for the trajectory tracking control of a robot manipulator using different optimization algorithms such as, Antlion Optimization Algorithm (ALO) Sine Cosine Algorithm (SCA) Grey Wolf Optimizer (GWO) and Whale Optimizer Algorithm (WOA). The aim of this work is to introduce a novel SMC-PID-ALO to control nonlinear systems, especially the position of two of the joints of a 2DOF robot manipulator. The basic idea is to determinate four optimal parameters (Kp, Ki, Kd and lamda) ensuring the best performance of a robot manipulator system, minimizing the integral time absolute error criterion (ITAE) and the integral time square error criterion (ISTE). The robot manipulator is modeled in Simulink and the control is implemented using the MATLAB environment. The obtained simulation results prove the robustness of ALO in comparison with other algorithms.

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

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.

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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