Dexterous robotic manipulation via a dynamic sliding mode force/position control with bounded inputs

Pliego‐Jiménez, Javier; Arteaga, Marco A.; Sánchez-Sánchez, Pablo

doi:10.1049/iet-cta.2018.5331

Cited by 9 publications

(6 citation statements)

References 58 publications

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“…Despite the intriguing performance of aforementioned adaptive NN methods, the constraints of actuators are rarely considered, which, if not properly coped, may severely degrade system performances and even cause instability [15,16]. The controllers proposed in reference [17,18] are bounded, but the boundary of the control signal cannot be determined by the users; that is, whether this bounded input satisfies the limits of the actuator or not is unknown and unmodifiable [19]. It is of practical importance to constrain the control signal within the physical limits before it is sent to the actuator.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Zhang,

Zhao,

Wang

et al. 2024

IET Control Theory & Appl

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This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend‐state‐observer is utilized to estimate the velocity signals; then, a neural‐network‐based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural‐network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade‐off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two‐degree‐of‐freedom robot manipulator.

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Section: Introductionmentioning

confidence: 99%

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Zhang,

Zhao,

Wang

et al. 2024

IET Control Theory & Appl

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“…Most soft multiple fingers use an adaptive open-loop control method for grasping [ 6 , 7 ]. While most researchers have focused on the contact point between the manipulator and the object [ 8 , 9 ], few have considered the friction issue upon the contact point [ 10 ]. The research on contact constraint is still insufficient, and current torque constraint investigations mainly focus on rigid contact assumptions, ignoring the elastic process during grasping [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling Contact Stiffness of Soft Fingertips for Grasping Applications

Ma,

Chen,

Gao

et al. 2023

Biomimetics

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Soft fingertips have distinct intrinsic features that allow robotic hands to offer adjustable and manageable stiffness for grasping. The stability of the grasp is determined by the contact stiffness between the soft fingertip and the object. Within this work, we proposed a line vector representation method based on the Winkler Model and investigated the contact stiffness between soft fingertips and objects to achieve control over the gripping force and fingertip displacement of the gripper without the need for sensors integrated in the fingertip. First, we derived the stiffness matrix of the soft fingertip, analyzed the contact stiffness, and constructed the global stiffness matrix; then, we established the grasp stiffness matrix based on the contact stiffness model, allowing for the analysis and evaluation of the soft fingertip’s manipulating process. Finally, our experiment demonstrated that the variation in object orientation caused by external forces can indicate the contact force status between the fingertip and the object. This contact force status is determined by the contact stiffness. The position error between the theoretical work and tested data was less than 9%, and the angle error was less than 5.58%. The comparison between the theoretical contact stiffness and the experimental results at the interface indicate that the present model for the contact stiffness is appropriate and the theoretical contact stiffness is consistent with the experiment data.

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“…Attitude stabilization and tracking are important objectives in automatic control [ 1 ] and robotics, since these problems appear in several tasks, such as aerospace tasks, robot force control, dexterous robot manipulation, assembly tasks, and vehicle orientation [ 2 , 3 , 4 , 5 ]. Compared to position trajectory tracking or regulation problems, attitude control is more involved.…”

Section: Introductionmentioning

confidence: 99%

Attitude Synchronization of a Group of Rigid Bodies Using Exponential Coordinates

Sidón-Ayala,

Pliego-Jiménez,

Cruz-Hernandez

2023

Entropy

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Currently, managing a group of satellites or robot manipulators requires coordinating their motion and work in a cooperative way to complete complex tasks. The attitude motion coordination and synchronization problems are challenging since attitude motion evolves in non-Euclidean spaces. Moreover, the equation of motions of the rigid body are highly nonlinear. This paper studies the attitude synchronization problem of a group of fully actuated rigid bodies over a directed communication topology. To design the synchronization control law, we exploit the cascade structure of the rigid body’s kinematic and dynamic models. First, we propose a kinematic control law that induces attitude synchronization. As a second step, an angular velocity-tracking control law is designed for the dynamic subsystem. We use the exponential coordinates of rotation to describe the body’s attitude. Such coordinates are a natural and minimal parametrization of rotation matrices which almost describe every rotation on the Special Orthogonal group SO(3). We provide simulation results to show the performance of the proposed synchronization controller.

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Dexterous robotic manipulation via a dynamic sliding mode force/position control with bounded inputs

Cited by 9 publications

References 58 publications

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

Modeling Contact Stiffness of Soft Fingertips for Grasping Applications

Attitude Synchronization of a Group of Rigid Bodies Using Exponential Coordinates

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