## 4 Development of NCS from 2011 Onwards  
In the last decade, highly developed control methods were employed for improving the performance of NCS in terms of system stability, delays, event-triggering algorithms, network security, etc. In addition, the discrete time NCSs have received a considerable amount of attention. Model-based predictive NCSs (MBPNCSs) were proposed by Reference [84] that compensated for the random delays and for the data losses in data transmission. The performance of the system was improved with the help of a predictive control scheme. In Reference [85], an approach for stabilization via discontinuous Lyapunov function was proposed, where sampling intervals that varied in terms of their nature, the dropouts, and  
Figure 4: Distribution of focus areas of NCS research from 2001 to 2010. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S.No.** & **Papers** & **Parameter/Area/Network** \\ \hline
1 & [32, 35, 38, 46, 47, 79] & Scheduled Networks \\ \hline
2 & [34, 36, 39, 41, 46, 47, 49, 51, 57, 62, 71, 76, 81] & Stability Analysis/Approach \\ \hline
3 & [37, 42, 46, 47, 75] & Complex/Interactive Networks \\ \hline
4 & [10, 43, 46, 63] & Internet/Communication Based Multi-rate Control Networks \\ \hline
5 & [46, 53, 75, 78] & Event Based Networks \\ \hline
6 & [33, 44, 45, 46, 48, 50, 52, 54, 56, 61, 65, 66, 67, 70, 72, 74, 77, 80, 82] & Delay/Fault Detection/Packet Loss \\ \hline
7 & [46, 58, 83] & Autonomous Networks \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summarized discussion of development of NCS from 2001 to 2010.  
network delays were taken into account. In Reference [86], a \(H\)Z/\(H\infty\) control of random time delayed networked systems was proposed. In Reference [87], the stability investigation of a NCS was achieved, where the communication between the controller and the plant input is through a digital channel, with dropouts in data-packets and finite-level quantization. Again, in Reference [88], authors describe the stability of NCSs with respect to time-varying transmission intervals. For discrete-time models, in Reference [89], the difficulty of the networking-based \(H\infty\) filtering method was described. A Markov jumping model based method was described to design \(H\infty\) filters, in which the filter gains depend on both the network delays and dropouts in data-packets. In Reference [90], a novel method was presented to calculate the stability of continuous linear systems with input containing the sampled data. In a technical note, Reference [91] presented a new method with control packet loss. In Reference [92], a networked control loop was considered in which the \"slave\" portion was a plant, and the \"master\" portion was the remote controller and observer. In networks, the fault detection of linear systems with restricted loss of data packets was proposed by Reference [93]. In Reference [94], a modern control system mapped on Networks-on-Chip was presented. The proposed architecture was supposed to play a pivotal role in real time applications like missile control system, robot trajectory, and satellite vehicle orbital trajectory control system. In Reference [95], a NCS framework for the coordinated control of distributed generation sources in smart power grids was proposed. The system measurements were transmitted to the controller through a real-time communication network and the effects of delays and packet dropouts due to the communication network were modeled. An event-triggered transmission scheme was proposed by Reference [96] for a sampled-data control system. In Reference [97], an event-triggering method was designed to decrease the network communication load, which could also be used to decide the time moment of the sampled signals. In Reference [98], a methodology for the technological investigation of iterative learning control for sampled-data systems was proposed. In Reference [99], a new Lyapunov functional was constructed to drive some stability criteria.