Introduction to robotics: Mechanics and control

Rodd, M.G.

doi:10.1016/0005-1098(87)90105-1

Cited by 28 publications

(23 citation statements)

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“…Both the above assumptions are quite reasonable regarding the boundedness of uncertainties. The first and second time-derivatives of the system uncertainties/disturbances are assumed to be bounded (Craig, 2005). However, the above assumption does not hold for some practical systems with unmodeled dynamics and input saturation.…”

Section: Robotic Manipulator: a Dynamic Modelmentioning

confidence: 99%

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

Rauf

Zhao

Khan

et al. 2022

Transactions of the Institute of Measurement and Control

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In this paper, the problem of trajectory tracking for robotic manipulator with input deadzone and time-varying disturbances is investigated using a continuous non-singular terminal sliding mode control (SMC). The robotic manipulator plays a vital role in industrial as well as many other applications. However, it faces numerous problems for accurate and fast trajectory tracking response in the presence of unknown uncertainties and external disturbances, that always have adverse affects on system closed-loop performance. This paper proposes a valid control design scheme to address the aforementioned practical issue. First, a non-singular terminal sliding mode surface is designed. Then, a robust control law is developed to guarantee the finite-time converges of system states to the origin. In addition, the sign function is substituted by saturation function, which can effectively reduce the chattering driven by the high-frequency switching item in the traditional SMC method. Moreover, the Lyapunov stability criterion is used to prove the stability of the closed-loop system. The efficiency of the designed control scheme is validated with numerical simulations.

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Section: Robotic Manipulator: a Dynamic Modelmentioning

confidence: 99%

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

Rauf

Zhao

Khan

et al. 2022

Transactions of the Institute of Measurement and Control

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“…Unlike traditional discrete robots, continuous robots do not have discrete joints or rigid links, and cannot use traditional kinematics modeling methods. 31 Obviously, the pneumatic driven robot hand selfdeveloped is a continuum robot (Figure 2(c)). After decades of development, modeling methods of continuous robots are mainly composed of the following two kinds: the piecewise constant curvature approximate model 32 and the experimental model.…”

Section: The Pose Representation Of Kinematics Modelmentioning

confidence: 99%

Kinematics modeling and grasping experiment of pneumatic four-finger flexible robotic hand

Liu

Geng

2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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To solve the complex structure, poor flexibility, and heavyweight of the rigid robotic hand, a pneumatic four-finger flexible robotic hand is developed in this paper. The robotic hand is about 1.3 times as large as that of a human hand and each finger is composed of a single multi-drive bending joint. The kinematic model of the robotic hand is established by using homogeneous coordinate transformation matrix. Through the simulation experiment of the robotic hand structure, the trajectory, and workspace of the robotic hand are established. According to the experimental results of grasping performance of the robotic hand, the grasping forces of different geometric positions along the finger axis are obtained. The results show that the robotic hand can realize a variety of grasping modes, has flexible action and strong adaptive ability; it can grasp, hold, and pinch, as well as stably grasp objects such as cylinder, box, and sphere. In pinch grasp mode, the robotic hand can grip objects as thin as 1 mm and the diameter of the grasped object varies from 28 mm to 160 mm; the maximum mass that the robotic hand can grasp an object with a diameter of 90 mm under 0.35 MPa is 1386 g.

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“…This section provides a two‐link manipulator as a simulation example to testify the proposed robust controller () and the IFT optimization algorithm 33 . Figure 3 shows the model of the manipulator, and Table 1 lists the corresponding parameters.…”

Section: Numerical Examplementioning

confidence: 99%

Iterative feedback tuning for optimal repetitive constraint‐following control of uncertain mechanical systems using Udwadia–Kalaba theory

Huang

Zhao

et al. 2021

Optim Control Appl Methods

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Introduction to robotics: Mechanics and control

Cited by 28 publications

References 0 publications

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

Finite-time robust control of robotic manipulator with input deadzone and time-varying disturbance compensation

Kinematics modeling and grasping experiment of pneumatic four-finger flexible robotic hand

Iterative feedback tuning for optimal repetitive constraint‐following control of uncertain mechanical systems using Udwadia–Kalaba theory

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