Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments

Smith, Alex; Yang, Chenguang; Ma, Hongbin; Culverhouse, Phil F.; Cangelosi, Angelo; Burdet, Etienne

doi:10.1371/journal.pone.0129281

Cited by 35 publications

(22 citation statements)

References 32 publications

(31 reference statements)

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“…In the first trial, the reference trajectory should be set to be periodic to satisfy the PE condition. From the second trial, for a better tracking performance, the reference point changes in a manner as in [52] as below , and it will be adapted iteratively. On the other hand, x d (t) is the reference trajectory used by the robot in practice.…”

Section: A Adaptation Of Reference Point and Feedforward Forcementioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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Section: A Adaptation Of Reference Point and Feedforward Forcementioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

146

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show abstract

“…The experiment shows the stability and the fast response to any action. Experiments have been conducted on manipulator controllers, where the fuzzy logic is used to control the joints of the robot (Smith et al (2015), McLauchlan et al (1997), Lea et al (1993) and Malki et al (1997)). …”

Section: Stereo Vision Based Object Tracking Control For a Movable Romentioning

confidence: 99%

Stereo Vision based Object Tracking Control for a Movable Robot Head

2016

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In this paper, we have developed a visual tracking control system on a purposely built movable robot head mounted on a Baxter robot. The tracking control algorithm used a stereo vision system provided by a Bumblebee camera, facilitated by the MATLAB computer vision toolbox. The objective of this work is to enable a robot to move its head as in a similar manner as our humans beings to focus vision on a moving object. Identification of object and estimation of its coordinate are performed based on image processing techniques. A fuzzy logic technique is applied to control the moving head in order to bring human like motion into the robot. The method developed in this work can be extended to any other movable robot head platform. Extensive experimental studies have been performed to test the effectiveness and efficiency of the proposed method.

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“…A number of research results have shown that for any continuous smooth function ϕ(z) : Ω z → R over a compact set Ω z ⊂ R Nn [32], [33], we can apply RBFNN (9) to approximate ϕ(z). In particular, if N s is chosen a sufficiently large value, such that the ideal bounded weight W * exists, we have…”

Section: A Rbfnn Approximationmentioning

confidence: 99%