Robust neural network control of robotic manipulators via switching strategy

Yu, Lei; Fei, Shumin; Huang, Jun; Li, Yongmin; Yang, Gang; Sun, Lining

doi:10.14736/kyb-2015-2-0309

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…where M xẍ ,ḟ andẋ s have been replaced by the equivalent expressions from the closedloop system (30) and Property 2.2 has been used. Then, in accordance with (27) and (13),V (f ,x s ,ẋ) ≤ 0 and it is demonstrated that the equilibrium vector of (30) is stable in the Lyapunov sense. Moreover, LaSalle's invariance theorem [12] can be used by defining the following set…”

Section: Hybrid Force/vision Control Schemementioning

confidence: 73%

“…Over the years, the robotic tasks have been evolving into more complex areas and this causes that robotic systems require of different kind of sensors, as a way to obtain information of their surroundings and their own condition [1,18,24,27,28,29]. Particularly, robotic systems that work in unstructured environments require information that allows them to navigate and interact with the environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A generalised proportional-derivative force/vision controller for torque-driven planar robotic manipulators

Vidrios‐Serrano¹,

Mendoza²,

Bonilla³

et al. 2020

Kybernetika

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Section: Hybrid Force/vision Control Schemementioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

A generalised proportional-derivative force/vision controller for torque-driven planar robotic manipulators

Vidrios‐Serrano¹,

Mendoza²,

Bonilla³

et al. 2020

Kybernetika

View full text Add to dashboard Cite

show abstract

A Dynamic Neural Network Approach for Efficient Control of Manipulators

Shao

Guan

2019

IEEE Trans. Syst. Man Cybern, Syst.

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ANFIS+PID Hybrid Controller Design for Controlling of a 6-DOF Robot Manipulator and its Error Convergence Analysis

طلب¹,

میگلی²,

غفاری³

2019

JoC

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In this paper, an ANFIS+PID hybrid control policy has been addressed to control a 6degree-of freedom (6-DOF) robotic manipulator. Then its error convergence has been also evaluated. The ability to formulate and estimate the system uncertainties and disturbances along with system dynamics and rejecting the disturbances effect are some advantages of the proposed method in comparing with the conventional ANFIS structures. The error convergence could not be proved in the ordinary ANFIS structures. But in the proposed method, the error convergence of the robot manipulator can be established under considering some mathematical conditions. The proposed control law is realized via parallel combination of ordinary ANFIS network and PID controller. The suggested method has been successfully applied in a 6-DOF robot manipulator system. Furthermore, in presence of uncertainties and external disturbances error convergence would be justified using the Lyapunov-like theorem and Barbalat lemma.

show abstract

Robust neural network control of robotic manipulators via switching strategy

Cited by 3 publications

References 17 publications

A generalised proportional-derivative force/vision controller for torque-driven planar robotic manipulators

A generalised proportional-derivative force/vision controller for torque-driven planar robotic manipulators

A Dynamic Neural Network Approach for Efficient Control of Manipulators

ANFIS+PID Hybrid Controller Design for Controlling of a 6-DOF Robot Manipulator and its Error Convergence Analysis

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