scholarly journals Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
M. J. Mahmoodabadi ◽  
N. Nejadkourki
Keyword(s):  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Flexible Robot ◽  
Dynamical Equations ◽  
Control Effort ◽  
Adaptive Sliding Mode ◽  
Multiobjective Particle Swarm Optimization ◽  
Fuzzy Adaptive

This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.

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.

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.

Fuzzy adaptive sliding mode control of a direct drive robot

1996 ◽  
Vol 19 (2) ◽  
pp. 215-227 ◽  
Author(s):  
Kemalettin Erbatur ◽  
Okyay Kaynak ◽  
Asif Sabanovic ◽  
Imre Rudas
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Adaptive Sliding Mode Control ◽  
Direct Drive ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Fuzzy Adaptive

scholarly journals Design of Adaptive Sliding Mode Controller for Uncertain Pendulum System

2021 ◽  
Vol 39 (3A) ◽  
pp. 355-369
Author(s):  
Dina H. Tohma ◽  
Ahmed K. Hamoudi
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
External Disturbances ◽  
Control Effort ◽  
Pendulum System ◽  
Adaptive Sliding Mode ◽  
Adaptive Sliding Mode Controller ◽  
Parameters Uncertainty ◽  
Better Than

This work aims to study and apply the adaptive sliding mode controller (ASMC) for the pendulum system with the existence of the parameters uncertainty, external disturbances, and coulomb friction. The adaptive sliding mode controller has several features over the conventional sliding mode control method. Firstly, the magnitude of the control signal is reduced to the minimally acceptable level defined by special conditions concerned with ASMC algorithm. Secondly, the upper bounds of uncertainties are not necessary to be defined before starting the work. For this reason, the ASMC can be used successfully to control the pendulum system with minimum control effort. These properties of the ASMC are confirming graphically by the simulation results using MATLAB 2019. The ASMC achieves an asymptotically stable system better than the Classical Sliding Mode Controller (CSMC). The unwanted phenomenon is called “chattering", which is appearing in the control action signal. These drawback properties are suppressed by employing a saturation function. Finally, the comparison between the results of the ASMC and CSMC showed that ASMC is the better one.

Sign in / Sign up

Export Citation Format

Share Document