Real-Time Implementation of Sliding Mode Control Technique for Two-DOF Industrial Robotic Arm

Ayten, Kağan Koray; Dumlu, Ahmet

doi:10.21597/jist.396344

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Step 2. Derive the relationship between joint motions and modal coordinates of the medical manipulator by formulas ( 6) and (7), respectively, producing the modal coordinates of the manipulator under the current joint trajectory; Solve the displacement variable at the end by formula (25); Calculate the fitness of each particle by formula (15).…”

Section: Design Of Adaptive Pso Algorithmmentioning

confidence: 99%

“…Recently, medical robots and other medical equipment have attracted much attention. To assist doctors in surgeries, medical manipulators must be highly accurate and flexible, and good at jitter suppression [7][8][9], ensuring the stability and continuity of velocity and acceleration. Before the surgery, the manipulator trajectory must be optimized to minimize the impact on each joint, and to precisely reach the designated positions [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

Gao¹,

Pan²

2020

JESA

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With the development of smart healthcare, surgical and rehabilitation robots have permeated into daily medical operations. This raises concerns over the trajectory planning of medical manipulators. Based on particle swarm optimization (PSO) algorithm and fuzzy neural network (FNN), this paper puts forward a trajectory planning algorithm for medical manipulators, which ensures that the target medical manipulator can suppress the residual jitter at the end, while meeting the requirements of high precision, flexible operation, and disturbance resistance. Specifically, a kinetic model was constructed for a medical manipulator of multi-degrees-of-freedoms (DOFs) through position and posture transforms, and used to construct an FNN for trajectory planning. To suppress the jitter at the end, an adaptive PSO algorithm was designed, and combined with the FNN into a trajectory planning algorithm called PSO neural network (PSONN) algorithm. Finally, the proposed algorithm was proved effective through experiments. The research results provide the reference for applying PSO algorithm and FNN in other fields.

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Section: Design Of Adaptive Pso Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

Gao¹,

Pan²

2020

JESA

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“…Furthermore, industrial maneuvers find utility in welding, assembling, manufacturing, painting, and various operations within the automotive industry, as well as in the handling of radioactive and biohazardous materials during robotically assisted surgery. The imperative for enhanced efficiency and high-quality manufacturing has driven the pursuit of more robust and sophisticated robotic maneuvers 1 – 4 .…”

Section: Introductionmentioning

confidence: 99%

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Azeez,

Abdelhaleem,

Elnaggar

et al. 2023

Sci Rep

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The aim of this study is to enhance the performance of a nonlinear three-rigid-link maneuver (RLM) in terms of trajectory tracking, disturbance and noise cancellation, and adaptability to joint flexibility. To achieve this, an optimized sliding mode controller with a proportional integral derivative surface (SMC-PID) is employed for maneuver control. An improved artificial bee colony algorithm with multi-elite guidance (MGABC) is utilized to obtain optimal values for the sliding surface and switching mode gain and attain the best performance for the robot maneuver system. The selection of the MGABC algorithm is based on its efficient exploration and exploitation techniques. The performance of the optimized SMC-PID robotic system is compared against other optimization algorithms found in existing literature, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Ant Lion Optimizer (ALO), and Grey Wolf Optimizer (GWO). The implemented controller effectively reduces the tracking error to 0.00691 radians, eliminates chattering phenomena in the control effort, and demonstrates robustness against disturbances and noise. The controller ensures that the objective function (OBJF) is minimized, with 0.954% increase in OBJF under low disturbance and noise conditions and 14.55% under severe disturbance and noise conditions. Moreover, the optimized controller exhibits resilience to variations in payload mass analysis, with the percentage increase in OBJF values ranging from 5.726% under low uncertainty conditions to 18.887% under severe uncertainty conditions. Flexible-link maneuvers (FLM) offer advantages such as improved safety and increased operating speeds in real-world applications. In this study, we investigated the impact of joint flexibility on the performance of the FLM system. Our proposed controller demonstrated superior tracking performance, characterized by minimal vibrations in the movement of the end effector.

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“…The backlash that occurred in the typical gearing contributes to a "dead zone" falling in the region of the system null point and reducing the accuracy in the position [24][25][26][27]. Uecker et al [28] demonstrate that methods based on the model achieve two to four times better performance than the Proportional-Integral-Derivative (PID); Ayten and Dumlu [29] address the tracking control of a 2 DOF DD arm with adaptive and robust control schemes; Azizi and Yazdizadeh [30] present a systematic comparison of passivity-driven robust solution for a 2 DOF DD robot arm; Santibanez et al [31] demonstrates the effect of static friction on the set-point control of a DD system. An important problem with DD actuators is the non-uniform distribution of the motor windings, the saliencies in the rotor/stator, and their interaction with the winding currents giving rise to undesirable torque pulsations.…”

Section: Introductionmentioning

confidence: 99%

Design and Stability Analysis of a Robust-Adaptive Sliding Mode Control Applied on a Robot Arm with Flexible Links

Uyulan

2021

Vibration

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Modelling errors and robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode-based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids the excitation of unmodeled dynamics. The main purpose of this paper was to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges were compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely tracking the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision and robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics. The following highlights are covered: A 2-DOF flexible robot arm considering actuator dynamics was modelled; the theoretical implication of the chattering-free sliding mode-adaptive linearizing algorithm, which ensures robust stabilization and precise tracking control, was designed based on the full system model including actuator dynamics with computer simulations. Stability analysis of the zero dynamics originated from the Lyapunov theorem was performed. The conceptual design necessity of nonlinear observers for the estimation of immeasurable variables and parameters required for the control algorithms was emphasized.

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Real-Time Implementation of Sliding Mode Control Technique for Two-DOF Industrial Robotic Arm

Cited by 5 publications

References 16 publications

A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

A Trajectory Planning Algorithm for Medical Manipulators Based on Adaptive Particle Swarm Optimization and Fuzzy Neural Network

Optimized sliding mode controller for trajectory tracking of flexible joints three-link manipulator with noise in input and output

Design and Stability Analysis of a Robust-Adaptive Sliding Mode Control Applied on a Robot Arm with Flexible Links

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