# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
## 4 Model Mediation
### Model Mediated Systems
#### 4.2.2 Common Systems  
In [68], authors developed a feedback predictive controller based on the recurrent neural network (RNN) for the dynamic bilateral teleoperation systems. The recurrent neural network controller is used to compensate for the time delay and result in a delay free system. The system employs the neural network predictor at the slave device side to predict its behavior; mainly for position-to-position teleoperation systems. This predictor is a non-linear one, which means that the relation between the slave inputs (i.e., position and velocity) and the slave outputs (i.e., force) is non-linear. The system employs another predictor at the master side, which is a linear predictor. The master side predictor is a modified Smith predictor. Figure 5 illustrates the structure of the recurrent neural network-based bilateral teleoperation system with both predictors at the distant sides. The slave side also deploys a nonlinear compensator to compensate for the non-linearity and match both predictors at the distinct sides. The system uses a common non-linear environmental model at the slave side, which is the Hunt-Crossley model, for identifying input-output response of the remote environment [58]. The system achieves the stability requirements for the closed loop feedback control system. The main advantage of the system is that it assumes the nonlinearity relationship of the system parameters in environmental modeling, which enables the time varying behavior. Furthermore, the system deploys a compensator, which compensate for the miss match between both deployed predictors. The system does not require pre-defined environmental parameters, which can be considered as a great advantage. The system is suitable only for systems with constant time delay communication channel. In [71], a model mediated teleoperated system was developed based on linear predictor. The system is introduced mainly to reduce the packet rate of data exchanged between the master and the slave sides of the bilateral teleoperated systems without affecting the user experience of the system. Experimental measurements proved that the proposed algorithm can reduces the packet rate with 90% for systems with 3 DoF. Employing traditional compression techniques for haptic data is not applicable; this is due to the large delay introduced by the building blocks of coding techniques. One way to reduce the number of transmitted packets is by making use of the properties of the human haptic perception. Thus, the system transmits only haptic events that a human being can perceive, based on the [PERSON]'s law for human haptic perception approximation [72]. [PERSON]'s law states that the relation between the human stimulus intensity (\(E\)) and the smallest change of the still perceivable stimulus intensity (\(\Delta E\)) is linear. \[\Delta E=CE \tag{1}\]  
The constant (C) are referred to as the just noticeable difference (JND) or the differential stimulus threshold, which can be measured experimentally as a statistical value rather quantitative. Using JND, the system updates the haptic data and transmits it, only when the change of a haptic stimulus is detectable. Figure 6 illustrates the structure of the system, where the system employs two linear coherent predictors on both network sides. These predictors not only help the master and slave devices to  
Figure 5: End-to-end system structure of the RNN-based model mediated bilateral teleoperated system developed in [68]. overcome the problem of communication delay associated with the haptic communication, but also used mainly to reduce the transmitted packets between both sides. Since the two predictors are coherent, the haptic data are transferred only if the actual data are different from the predicted ones. The system did not consider the deployment of a visual sensor or other impedance-based sensors in the slave side as the work is only a mathematical function-based algorithm, which is the main disadvantage of the work.