Trajectory Planning and Second-Order Sliding Mode Motion/Interaction Control for Robot Manipulators in Unknown Environments

Capisani, Luca Massimiliano; Ferrara, Antonella

doi:10.1109/tie.2011.2160510

Cited by 108 publications

(54 citation statements)

References 27 publications

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“…Because of the continuous nature of the control action, the HOSM control approach is appropriate to be applied even to electromechanical or mechanical systems [16], [17], as testified by [18], [19], [20]. Yet, as highlighted in [11], some problems remain during the transient phase, i.e.…”

mentioning

confidence: 99%

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding Modes

Ferrara

Incremona

2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper deals with the formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm oriented to solve motion control problems for robot manipulators, taking into account the presence of unavoidable modelling uncertainties and external disturbances affecting the systems. The proposed algorithm is designed so that the socalled reaching phase, normally present in the evolution of a system controlled via a Sliding Mode controller, is reduced to a minimum. Moreover, since the relative degree of the relevant system output is suitably augmented through the use of an integrator, the control action affecting the robotic system is continuous, with a significant benefit, in terms of chattering alleviation, for the overall controlled electromechanical system. The verification and validation of our proposal have been performed by simulating the motion control scheme relying on a model of the considered robot, i.e. a COMAU SMART3-S2 anthropomorphic industrial robot manipulator, identified on the basis of real data.

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confidence: 99%

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding Modes

Ferrara

Incremona

2013

52nd IEEE Conference on Decision and Control

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“…The key problems in the last twenty years were force analysis [5] and grip strength [6], usually problem occurs when multi-fingered grasping takes place. Generally speaking, advances in the recognition of human activities such as motion control [7], hand grasping [8][9] and robot grasping [10] are progressing. They are demonstrated using popular methods such as EMG [11], Dataglove [12][13][14] [15], and humanoid hand [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Gesture Recognition Based on Human Grasping Activities Using PCA-BMU

Adnan¹,

Mahzan²

2015

ijacsa

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Abstract-This research study presents the recognition of fingers grasps for various grasping styles of daily living. In general, the posture of the human hand determines the fingers that are used to create contact between an object at the same time while developing the touching contact. Human grasping can detect by studying the movement of fingers while bending during object holding. Ten right-handed subjects are participated in the experiment; each subject was fitted with a right-handed GloveMAP, which recorded all movement of the thumb, index, and middle of human fingers while grasping selected objects. GloveMAP is constructed using flexible bend sensors placed back of a glove. Based on the grasp human taxonomy by Cutkosky, the object grasping is distinguished by two dominant prehensile postures; that is, the power grip and the precision grip. The dataset signal is extracted using GloveMAP, and all the signals are filtered using Gaussian filtering method. The method is capable to improving the amplitude transmission characteristic with the minimal combination of time and amplitude response. The result was no overshoot in order to smoothen the grasping signal from unneeded signal (noise) that occurs on the input / original grasping data. Principal Component Analysis -Best Matching Unit (PCA-BMU) is a process of justifying the human grasping data involves several grasping groups and forming a component identified as nodes or neuron.

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“…Controlling the human robot interaction force offers a solution and implementing impedance control [8] with use of force sensor showed feasibility of collaboration without fences. Another control scheme is presented by [9] with the comparison of PD and sliding mode controller algorithms. The other possibility to overcome this issue is to develop compliant robot systems.…”

Section: Robot Operation In Shared Spacementioning

confidence: 99%

CRM TC covering paper - Robotics trends

Tajti

Szayer

Kovács

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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-Previously, robots were mainly used by big companies in high volume manufacturing (i.e.: car industry). Industrial robots were perfect to execute procedures simplified to tiny (angular) movements. Today robots have appeared in small and medium enterprises, and they will appear rapidly in nonengineering fields as well, like offices, hospitals, and even in our homes. Robotics grains ground in newer and newer fields and it is too wide to overview it in one paper. This paper tries to summarize the nowadays robotics trends from the point of view of industrial electronics and industrial informatics.

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Trajectory Planning and Second-Order Sliding Mode Motion/Interaction Control for Robot Manipulators in Unknown Environments

Cited by 108 publications

References 27 publications

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding Modes

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding Modes

Gesture Recognition Based on Human Grasping Activities Using PCA-BMU

CRM TC covering paper - Robotics trends

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