Decentralized neural block control for an industrial PA10-7CE robot arm

Garcia-Hernandez, Ramon; Sánchez, Edgar N.; Santibáñez, Víctor; Ruz-Hernández, José A.

doi:10.1109/ijcnn.2011.6033586

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…As an attempt to alleviate these problems, some approaches have been proposed in the literature, which can be categorized from the control structure into two types: 1) the gain-schedule (or serial) approach and 2) the parallel approach. The gainschedule approach uses various control schemes to tune the robust gains in order to alleviate chattering [4], [5], while the parallel approach adds a continuous control to the discontinuous robust control so as to smooth the discontinuity [6], [7]. We remove the step-discontinuous transition by smoothing out the control discontinuity in a thin boundary layer surrounding a compact set.…”

Section: Introductionmentioning

confidence: 99%

Robust GRBF Static Neurocontroller With Switch Logic for Control of Robot Manipulators

Mulero-Martínez

2012

IEEE Trans. Neural Netw. Learning Syst.

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A new Gaussian radial basis function static neurocontroller is presented for stable adaptive tracking control. This is a two-stage controller acting in a supervisory fashion by means of a switch logic and allowing arbitration between a neural network (NN) and a robust proportional-derivative controller. The structure is intended to reduce the effects of the curse of dimensionality in multidimensional systems by fully exploiting the mechanical properties of the robot manipulator. A new factorization of the Coriolis/centripetal matrix is used, leading to an NN model that is much smaller than the dynamic ones. By resorting to the extended multivariate Shannon theorem and the computation of the effective bandwidth of the revolute robot manipulators, the network parameters are tuned. Stability and convergence properties are analyzed. This provides the assurance of reliability and effectiveness to make such controller viable. A robot manipulator with two degrees of freedom is employed to study the adaptive features of the neural control algorithm. Finally, the effectiveness of the proposed method is compared to the nonadaptive case.

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Section: Introductionmentioning

confidence: 99%

Robust GRBF Static Neurocontroller With Switch Logic for Control of Robot Manipulators

Mulero-Martínez

2012

IEEE Trans. Neural Netw. Learning Syst.

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“…A NBC transformation form generates this u and is an ideal controller for this work due to the fact that the NBC is designed from a block structure, such as that given by equation (9).…”

Section: Controlmentioning

confidence: 99%

“…In [9], a RHONN structure is used to identify the robot arm dynamics to obtain a solution to the trajectory tracking problem for robotic manipulators. The RHONN learning is performed online by a Kalman filtering algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A Dual Neural Network as an Identifier for a Robot Arm

Alvarez-Rodríguez

Castañeda

2015

International Journal of Advanced Robotic Systems

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A novel dual recurrent neural network is presented and is used to identify the dynamics for a robot arm with threeDegrees of freedom (DoF) and trained with a filtered error algorithm. The dual neural network has a structure of two recurrent neural networks working simultaneously, fighting each other to obtain the best identification values, being the criteria for the selection of the vest values: the standard deviation for the identification error. The neural identifier provides important information to a nonlinear block control transformation form acting as a control law to solve the trajectory tracking problem for the robotic plant's behavior.Keywords Robot arm, dual recurrent neural network, standard deviation for the identification error, nonlinear block control transformation form

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