## 5 Latency Mitigation Strategies
### Control Latency Approaches  
Control latency is the delay in data transmission of control commands from the operator to the vehicle (i.e., on the downlink). A list of latency mitigation strategies for control is provided in Table 5. On the one hand, earlier researchers focused on mathematical models for accurately predicting and compensating for undelayed controller signals [45]. These methods are well known to be model-based approaches [69]. They are usually modelled according to control theorems, out of which the most extensively researched compensation methods are based on the perspective of passivity theory [44; 51; 114]. Passivity-based control methods predict the signals after the delay without knowledge of the magnitude of the delay. They ensure the stability and transparency of the bilateral teleoperation systems. A few examples include wave variable-based methods, scattering signals, time-domain passivity analysis (TDPA)-based methods [48; 77; 79; 80], and model-mediated teleoperation(MMT) [69; 80; 115]. The major drawback of these methods is that they require knowledge of system dynamics. Moreover, their performance degrades due to model errors and complicated conditions with variable time delay, large disturbances, or extensions to multilateral systems [69]. On the other hand, model-free approaches based on data-driven models have been developed. These methods have grown popular as they are more robust and do not require any knowledge of system dynamics [69]. They are adaptable and can deal with disturbances and uncertainties while making predictions. A popular example is neural networks (NNs) [69; 116]. Time-series prediction methods effectively mitigate the effects of control latency by predicting or imitating the operator behaviour [12]. They tend to  
Figure 7: Latency mitigation strategy classification. be more adaptable to non-linear data. In the literature, most studies employ or combine traditional NN methods such as auto-encoder (AE), variational auto-encoder (VAE), recurrent neural network (RNN), and long short-term memory (LSTM) for time-series data [12]. Among these, RNN [74; 117] and LSTM [55; 69; 74; 118; 119] are more suitable for time-series prediction. However, sometimes RNNs cannot learn long-term dependencies, whereas LSTMs have the advantage of retaining both long- and short-term memory. A limitation of LSTMs is that they are incompatible with dynamic output lengths and may require retraining [12]. More recent methods, for instance, sequence-to-sequence (Seq2 Seq) models [120] and generative adversarial networks (GANs) [121] have shown great performance for time-series prediction, and they can tackle these limitations. GANs learn the distributions of time series well and also adapt to new data, but might not capture dependencies. Meanwhile, Seq2 Seq has a better mapping of input and output data relationships. Other significant control prediction methods include state estimation filters, motion models, linear regression, proportional-derivative models, statistical models, and probabilistic models [116], among others. Linear regression is a simple approach with good accuracy for smooth and linear motion. However, it is prone to errors and overshooting [122]. State estimation filters, such as Kalman filters (and their extensions), are commonly followed by motion models and are robust against fluctuations [122]. However, they are sensitive to noise and computationally expensive. Proportional-derivative methods achieve stability without knowing system dynamics, but their robustness may come at the cost of transparency [45]. Statistical models represent the control loop in the form of equations; for example, auto-regressive models, moving average models, and their extensions. However, these methods are unsuitable for non-linear and dynamic data, which can be expected in the real world [12]. Other control approaches aim to minimise delay, including supervisor control, control autonomy augmented reality [113], and adaptive-based control [70; 113].