Abstract
This paper presents a new method for selecting the force-reflection gain in a position-force type bilateral teleoperation system. The force-reflection gain greatly affects the task performance of a teleoperation system; too small gain results in poor task performance while too large gain results in system instability. The maximum boundary of the gain guaranteeing the stability greatly depends upon characteristics of the elements in the system such as: a master arm which is combined with the human operator's hand and the environments with which the slave arm contacts. In normal practice, it is, therefore, very difficult to determine such maximum boundary of the gain. To overcome this difficulty, this paper proposes a force-reflection gain selecting algorithm based on artificial neural network and fuzzy logic. The method estimates characteristics of the master arm and the environments by using neural networks and, then, determines the force-reflection gain from the estimated characteristics by using fuzzy logic. In order to show the effectiveness of the proposed algorithm, a series of experiments are conducted under various conditions of teleoperation using a laboratory-made telerobot system.
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Abbreviations
- E :
-
average system error
- F h :
-
forces from the human operator
- F m :
-
forces applied to the master arm, (F m =F h +F r )
- F r :
-
reflected force, (F r =k f ×F S )
- F S :
-
contact force
- f j (·):
-
activation function for the jth node
- g k , g m , g z :
-
scaling factors for k +inff , m, and z, respectively
- K i :
-
fuzzy subsets for \(\tilde k_{ f}^*\)
- k f :
-
force-reflection gain
- k f (t):
-
force-reflection gain at time t
- k *infk :
-
inferred force-reflection gain (output of the fuzzy gain selector)
- k *infk (t):
-
inferred force-reflection gain at time t
- \(\tilde k_{ f}^*\) :
-
fuzzy variable for k *inff
- M i :
-
fuzzy subsets for \(\tilde m\)
- M O :
-
DC gain of the master arm
- m :
-
output of the NN1
- \(\tilde m\) :
-
fuzzy variable for m
- net j :
-
net input to the jth node
- O i :
-
output of the ith node
- S O :
-
DC gain of the slave arm
- t m :
-
target output of the mth node in the output layer
- W ji :
-
weight between jth node and ith node
- X m :
-
position of the master arm
- X S :
-
position of the slave arm
- Z e O:
-
DC gain of the environment
- Z i :
-
fuzzy subsets for \(\tilde z\)
- z :
-
output of the NN2
- \(\tilde z\) :
-
fuzzy variable for z
- α, β:
-
weighting factors for the decision maker
- α w :
-
momentum rate
- α j :
-
partial derivative of error, E, with respect to net j
- η:
-
learning rate
- \(\mu _K \left( {\tilde k_f^* } \right):\) :
-
membership function for the final output of the fuzzy rule
- \(\mu _K \left( {\tilde k_f^* } \right):\) :
-
membership function for the output of the kth rule
- Π:
-
decision function of the decision maker
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Cha, D.H., Cho, H.S. & Kim, S. Design of a force reflection controller for telerobot systems using neural network and fuzzy logic. J Intell Robot Syst 16, 1–24 (1996). https://doi.org/10.1007/BF00309653
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DOI: https://doi.org/10.1007/BF00309653