Nonlinear PD plus sliding mode control with application to a parallel delta robot

Boudjedir, Chems Eddine; Boukhetala, Djamel; Bouri, Mohamed

doi:10.2478/jee-2018-0048

Cited by 29 publications

(24 citation statements)

References 18 publications

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“…The main membership functionF 1 i is shown as (11). The singleton fuzzifier method is adopted for the secondary membership.…”

Section: The Tracking Controller Of Par4 Robotmentioning

confidence: 99%

“…In the literature [9], the adaptive controller based on the output is proposed for a Delta robot, which is applied to implement the trajectory tracking and is helpful to gain the fast tracking of the reference trajectory. In the literatures [10][11][12][13], the trajectory tracking controllers have taken the advantages of easy implementation and parameter convenient adjustment with PID controller, and most of the improvements are embodied in the PID parameters, meaning that a variety of online real-time adjusting strategies of PID parameters are introduced to enhance the adaptive ability of the controller. Thus the adaptive ability of such controller is greatly influenced by the designed parameter control law, which to some extent depends on the designer's experience, being not conducive to the widespread usage.…”

Section: Introductionmentioning

confidence: 99%

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Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Zhang¹,

Ming²

2021

IJCIS

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A dynamic Grey Wolf Optimizer (GWO) is proposed, noted as DGWO2, and a novel dynamic improved GWO algorithm is obtained after transferring the mutation operator and the eliminating-reconstructing mechanism to the DGWO2, noted as DMR-GWO2. A Type-2 fuzzy predictive compensation PID trajectory tracking controller based on the DMR-GWO2 is proposed to attain the tracking targets for the trajectory control of Par4 parallel robot. In the trajectory tracking controller, the DMR-GWO2 is designed for optimizing the Type-2 fuzzy logic controller which is in parallel with the PID controller to speed up the response of the parallel robot and to get the high adaptive capacity of the whole system. One input of the Type-2 fuzzy logic controller is the tracking error, while the other input is the sum of the derivative of the tracking error and the change rate of the expected angle. Finally, the experiments verify the effectiveness of the designed trajectory tracking controller and the tracking errors are small.

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“…The main membership functionF 1 i is shown as (11). The singleton fuzzifier method is adopted for the secondary membership.…”

Section: The Tracking Controller Of Par4 Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Zhang¹,

Ming²

2021

IJCIS

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“…In order to achieve good fault compensation, controllers capable of effectively compensating for faults are necessary to enable them to perform their task independently and realistically. In this regard, numerous experiments have been developed to compensate for fault such as robust control algorithms, including synchronization control [11], artificial neural networks (ANN) [12,13], sliding mode control (SMC) [14,15] and time delay control (TDC) [16,17]. SMC are well known for their robustness against unknown systems dynamics.…”

Section: Introductionmentioning

confidence: 99%

Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

et al. 2021

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This work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.

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“…Many control strategies have been successfully implemented in the Delta robot, such as PD control, PD plus feedforward control, 23 and PD plus sliding mode control. 24 However, the drawback of the aforementioned schemes is that they cannot learn from repetitive operations; as a consequence, the tracking performances do not improve through the iterations, contrary to the learning control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive robust iterative learning control with application to a Delta robot

Boudjedir

Boukhetala

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

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Nonlinear PD plus sliding mode control with application to a parallel delta robot

Cited by 29 publications

References 18 publications

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Par4 Parallel Robot Trajectory Tracking Control Based on DMR-GWO2 and Fuzzy Predictive

Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

Adaptive robust iterative learning control with application to a Delta robot

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