Stable Neurovisual Servoing for Robot Manipulators

Loreto, G.; Garrido, R.

doi:10.1109/tnn.2006.875993

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…Where the new time varying rate k is: (11) and used in (16), e(k) is de ned in (18), 0 < 0 1 2 <, so 0 < k 2 <, it is assumed that the uncertainty is bounded [13], [18], [19], [20], [21], [24], [25], [26], [37] where is the upper bound of the uncertainty (k), j (k)j < . Remark 1: Please note that e(k) = b…”

Section: Stability Of the Backpropagation Algorithmmentioning

confidence: 99%

Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network

Rubio

Angelov

Pacheco

2011

IEEE Trans. Neural Netw.

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Neural networks (NNs) have numerous applications to online processes, but the problem of stability is rarely discussed. This is an extremely important issue because, if the stability of a solution is not guaranteed, the equipment that is being used can be damaged, which can also cause serious accidents. It is true that in some research papers this problem has been considered, but this concerns continuous-time NN only. At the same time, there are many systems that are better described in the discrete time domain such as population of animals, the annual expenses in an industry, the interest earned by a bank, or the prediction of the distribution of loads stored every hour in a warehouse. Therefore, it is of paramount importance to consider the stability of the discrete-time NN. This paper makes several important contributions. 1) A theorem is stated and proven which guarantees uniform stability of a general discrete-time system. 2) It is proven that the backpropagation (BP) algorithm with a new time-varying rate is uniformly stable for online identification and the identification error converges to a small zone bounded by the uncertainty. 3) It is proven that the weights' error is bounded by the initial weights' error, i.e., overfitting is eliminated in the proposed algorithm. 4) The BP algorithm is applied to predict the distribution of loads that a transelevator receives from a trailer and places in the deposits in a warehouse every hour, so that the deposits in the warehouse are reserved in advance using the prediction results. 5) The BP algorithm is compared with the recursive least square (RLS) algorithm and with the Takagi-Sugeno type fuzzy inference system in the problem of predicting the distribution of loads in a warehouse, giving that the first and the second are stable and the third is unstable. 6) The BP algorithm is compared with the RLS algorithm and with the Kalman filter algorithm in a synthetic example.

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Section: Stability Of the Backpropagation Algorithmmentioning

confidence: 99%

Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network

Rubio

Angelov

Pacheco

2011

IEEE Trans. Neural Netw.

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“…Additionally, to provide robustness some approaches add a high frequency input in the controller which represents the principal disadvantage in the practical applications. In [22] is proposed a neuro-visual servoing control for a planar robot manipulator assuming that link lengths of the robot manipulator are uncertain. In order to avoid the drift in the parameter estimated and some possible overshoot in the estimated of the gravitational vector a neural network is used.…”

Section: Introductionmentioning

confidence: 99%

“…That is, because of the fact that the sliding mode condition is relegated to the first order time derivative of the sliding surface, the possibility of chattering in the closed loop is eliminated. Compared with other approaches [20,22,32] the low dimensional neural network is used to approximate the robot parametric uncertainty. Additionally, the estimated Jacobian matrix is proposed by the user, considering that the exact Jacobian matrix of the robot manipulator is a function of the articular joints.…”

Section: Introductionmentioning

confidence: 99%

Task-space neuro-sliding mode control of robot manipulators under Jacobian uncertainties

García-Rodríguez

Parra-Vega

2011

Int. J. Control Autom. Syst.

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Cartesian robot control is an appealing scheme because it avoids the computation of inverse kinematics, in contrast to joint robot control approach. For tracking, high computational load is typically required to obtain Cartesian robot dynamics. In this paper, an alternative approach for Cartesian tracking is proposed under assumption that robot dynamics is unknown and the Jacobian are uncertain. A neuro-sliding second order mode controller delivers a low dimensional neural network, which roughly estimates inverse robot dynamics, and an inner smooth control loop guarantees exponential tracking. Experimental results are presented to confirm the performance in a real time environment.

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“…In [4] NN-based method to approximate the uncertainties of gravitational torques in a image based tracking framework. However, the tracking error achieved by this method could not reach zero.…”

Section: Introductionmentioning

confidence: 99%

Robust neural control of robot-camera visual tracking

Cát¹,

Minh

2009

2009 IEEE International Conference on Control and Automation

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In this paper, we propose a new method to control a robot-camera visual tracking system to track a moving target so that the image feature of the target can match some desired one. In particular, we develop a new control algorithm to calculate the necessary joint torques. To deal with the dynamics and Jacobian uncertainty of the problem, an on-line learning neural network (NN) is used to approximate uncertain components and tune the control scheme to ensure the mismatch of the image feature vanishing to 0. We also prove the asymptotical stability of the proposed tracking method by using Lyapunov stability method.

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Stable Neurovisual Servoing for Robot Manipulators

Cited by 17 publications

References 20 publications

Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network

Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network

Task-space neuro-sliding mode control of robot manipulators under Jacobian uncertainties

Robust neural control of robot-camera visual tracking

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