Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Kapoor, Neha; Ohri, Jyoti

doi:10.1007/s40031-016-0216-x

Cited by 10 publications

(5 citation statements)

References 61 publications

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“…Mәsәlәnin hәlli üsulu. İstәnilәn sistem modeli üçün PİD tәnzimlәyicisinin ümumi strukturunun xәta siqnalının proporsional, inteqral vә differensial hәrәkәt qanunları әsasında tәyin olunduğu mәlumdur [11,12] İndi mәqsәd kinematikanın trayektoriya xәtalarını minimuma endirmәk mәqsәdilә hәr bir birlәşmә üçün uyğun 𝐾 , 𝐾 vә 𝐾 әmsallarını (22) tapmaqdır.…”

Section: İdarәetmә Sisteminin Qurulmasıunclassified

Üç Sәrbәstli̇k Dәrәcәli̇ Robot Mani̇pulyator Si̇stemi̇ Üçün Opti̇mal Pi̇d Tәnzi̇mlәyi̇ci̇si̇ni̇n Modellәşdi̇ri̇lmәsi̇

Aslan Mәmmәdova,

Novruz Әliyeva,

Әmiraslan Әliyeva

2024

EƏ

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. In this research work, the application and analysis of the optimization methodology based on the dynamic flying objects algorithm to design the robot manipulator based on the optimal PID controller is considered. Based on the dynamic analysis of robot manipulators, the interaction between the torques created by the actuators and the position and speed of the manipulator was investigated. The optimal PID control law obtained from the proposed algorithm is applied to the robot system. The proposed controller optimizes the trajectory of the robot's end-effector for input during variable time and hardens the robot against perturbing effects. To achieve a highly adaptive optimization process, the correct formulation of the utility function leads to optimal solutions. Three different objective functions were used in the process of optimization of control parameters in the robot system and their results were compared. In this work, a new debugging methodology for trajectory tracking in robotic manipulator systems is presented. Optimum benefit is obtained by using the proposed PID regulation law. The obtained results are satisfactory and competitive. It has been determined that the problems of the smooth control system in the places where the robot system is highly non-linear are regulated by classical methods.

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Section: İdarәetmә Sisteminin Qurulmasıunclassified

Üç Sәrbәstli̇k Dәrәcәli̇ Robot Mani̇pulyator Si̇stemi̇ Üçün Opti̇mal Pi̇d Tәnzi̇mlәyi̇ci̇si̇ni̇n Modellәşdi̇ri̇lmәsi̇

Aslan Mәmmәdova,

Novruz Әliyeva,

Әmiraslan Әliyeva

2024

EƏ

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“…) is a skew symmetric [39]; that is, the components η jk of N satisfy η jk = −η kj and assure the following equation [38]:…”

Section: Property IIImentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints' velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.INDEX TERMS robotic manipulator, robust exact differentiator, feedforward neural network, fast integral sliding mode control, LuGre friction model, autonomous articulated robotic educational platform.

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“…If these coefficients are tuned properly, the SMC can reject disturbances more desirably. Accordingly, the proposed controller (Self-tuning-FSMC) has powerful resistance and can solve the problems of both structured and unstructured uncertainties (Kapoor & Ohri, 2017;Medhaar et al, 2006). In (Anh et al, 2018), an adaptive fuzzy sliding mode controller (AFSMC) to demonstrate the stability of a robotic manipulator system using Lyapunov stability theorem was presented.…”

Section: Related Workmentioning

confidence: 99%

Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

2021

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Robotic manipulators are highly coupled multi-input multi-output (MIMO) nonlinear systems with uncertainties and highly time-varying dynamic capabilities. These characteristics make the trajectory control of a robotic manipulator very challenging. This paper presents the modeling and trajectory tracking control of a 3-DOF robotic manipulator using self-tuning fuzzy sliding mode controller (ST-FSMC). The stability of the system was investigated using the Lyapunov direct method. The controller was implemented using MATLAB/Simulink and its performance was evaluated. The simulation results show that the proposed controller removed chattering phenomena from the control effort and reduced the tracking error (average steady-state error) to 0.0036 rad. However, in the case of the conventional controllers, the average steady-state error increased to 0.0413 rad, 0.0044 rad, and 0.0053 rad for the Proportional-integral-derivative (PID) controller, Sliding mode controller (SMC), and Fuzzy sliding mode controller (FSMC), respectively. The conventional controllers (PID, SMC, and FSMC) were designed for the Aderajew Ashagrie

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Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Cited by 10 publications

References 61 publications

Üç Sәrbәstli̇k Dәrәcәli̇ Robot Mani̇pulyator Si̇stemi̇ Üçün Opti̇mal Pi̇d Tәnzi̇mlәyi̇ci̇si̇ni̇n Modellәşdi̇ri̇lmәsi̇

Üç Sәrbәstli̇k Dәrәcәli̇ Robot Mani̇pulyator Si̇stemi̇ Üçün Opti̇mal Pi̇d Tәnzi̇mlәyi̇ci̇si̇ni̇n Modellәşdi̇ri̇lmәsi̇

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

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