A proportional-derivative-double derivative controller for robot manipulators

Saab, Samer S.; Jaafar, Rayana H.

doi:10.1080/00207179.2019.1642518

Cited by 41 publications

(11 citation statements)

References 35 publications

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“…A common class of closedloop controllers employs slide-mode approach; see, e.g., [55,56] and the references therein. ere are also several optimal stochastic simultaneous multivariable controllers such a PD-type [57], and ones with gravity compensation [58], PID-type [59][60][61], and even PDD [62]. Although the works in [55][56][57]62] include experimental justification on the Barrett WAM, they do not deal with controlling its gripper.…”

Section: Controllermentioning

confidence: 99%

“…ere are also several optimal stochastic simultaneous multivariable controllers such a PD-type [57], and ones with gravity compensation [58], PID-type [59][60][61], and even PDD [62]. Although the works in [55][56][57]62] include experimental justification on the Barrett WAM, they do not deal with controlling its gripper. All of the aforementioned strategies and many others come with a number of unique features; however, they require deep comprehension of control theory and/or some knowledge of the statistical models.…”

Section: Controllermentioning

confidence: 99%

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Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.

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Section: Controllermentioning

confidence: 99%

Section: Controllermentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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“…Recently, a variant of PID controller attracts the attention of power system researchers and control practitioners, which is known as proportional integral derivative acceleration (PIDA) or proportional integral derivative double derivative (PIDD2) [4,7,[10][11][12][13][14][15][16][17][18][19][20]. The acceleration term in PIDA controller has improved the stability of the control system [16][17][18][19][20]. Besides, PIDA has four coefficients, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…more degree of freedom in comparison to the PID. In addition, the PIDA control design satisfies the more desired constraints/conditions of the real-time control system [17][18][19][20]. For noise effect elimination, we have considered the low-pass filter with each derivative terms [18].…”

Section: Introductionmentioning

confidence: 99%

“…For noise effect elimination, we have considered the low-pass filter with each derivative terms [18]. Further, PIDA can be used to achieve faster and smoother response for higher order system and also keeps settling time and overshoot in an acceptable limit [17][18][19]. In the literature, many researchers have applied the PIDA controller for LFC problem in power system networks, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Graphic RCRA‐PIDA tuning based on maximum sensitivity for automatic generation control of thermal and hydro power systems

Kumar

Hote

2020

IET gener. transm. distrib.

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The frequency control is the mandatory task in a modern power system because of load demand variation and the integration of renewable energy sources (RESs). The main objective of automatic generation control (AGC) scheme is to maintain the balance between generation and load demand. This study proposes a graphical robust characteristic ratio assignment (RCRA) approach for PIDA controller tuning based on maximum sensitivity (M s) for AGC. The basic feature of this graphical approach is that characteristic ratios (γ i) in RCRA scheme are calculated in terms of M s. The important part of this design is that the analytical expressions for coefficient gains of PIDA controller are derived in terms of γ i and system parameters. Here, the non-reheat steam-turbine and hydro-turbine based single and multi-area power systems are considered for validation of the proposed control scheme. The critical issue in controller design for a hydro-turbine based power system is the non-minimum phase behaviour. The robustness and performance of RCRA-PIDA controller are examined under system non-linearities, parametric uncertainty and disturbances. Further, the performance of proposed approach is evaluated in the presence of RESs. Finally, it is observed that the proposed control approach performs better in comparison to the recently published control schemes.

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Fuzzy logic based medical diagnostic system for hepatitis B using machine learning

et al. 2023

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A proportional-derivative-double derivative controller for robot manipulators

Cited by 41 publications

References 35 publications

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Graphic RCRA‐PIDA tuning based on maximum sensitivity for automatic generation control of thermal and hydro power systems

Fuzzy logic based medical diagnostic system for hepatitis B using machine learning

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