## 6 Conclusions  
The RTD-A controller is evolving very fast and can be used as an alternative for the classical PID controller. Since RTD-A has an independent tuning parameter for robustness it can handle the systems with uncertain behavior. The proposed simulation tool is the very basic implementation of the RTD-A controller. This tool can be used to approximate an over-damped higher order transfer function into a FOPDT system. Therefore, the proposed tool can be implemented on any higher order processes with or without dead time. The industrial processes can adopt this RTD-A controller with \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specifications** & **PID** & **Ogunnaike** & **Kariwala** & **GSO** & **GA** & **PSO** & **Firefly** & **GWA** \\ \hline IAE & 570.6 & 452.1788 & 327.21 & 291.1297 & 291.0047 & 291.6691 & 291.2998 & 291.0940 \\ \hline Rise Time (s) & 15.47 & 17.53 & 10.4164 & 8.4854 & 8.4855 & 8.4845 & 8.4855 & 8.4854 \\ \hline Settling Time (s) & 57.78 & 32.09 & 47.77 & 11.0437 & 11.0425 & 11.0412 & 11.0424 & 11.0424 \\ \hline Overshoot (\%) & 7.9623 & 0 & 0 & 0 & 0.3746 & 0.7189 & 1.0824 & 0.5502 \\ \hline Steady State Error (\(\mathrm{e_{ss}}\)) & 0.0756 & 0.0016 & 0.0015 & 0.0182 & 0.0175 & 0.0220 & 0.0178 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Example 2: Performance comparison of different tuning methods and PID. Figure 26: Example 2: Regulatory response for the pressure control station with optimization algorithms. more flexibility. In the case study, the proposed tool had experimented on an interacting two tank liquid level process and a pressure control application. The optimization tool was tested in both cases and obtained the optimized results for the controller parameters under different optimization algorithms. In order to understand the effect of the changes in each tuning parameter on the plant output, a separate tool was developed for the RTD-A tuning parameter analysis and it was experimented on the interacting liquid level process. The closed loop stability of the liquid level control and pressure control was checked in the state variable form and also using the Jury's stability criterion. It was found that the controller guaranteed the closed loop stability. Finally, the closed loop process performance with the optimal tuned RTD-A controller using the firefly algorithm was better than other methods in terms of IAE. The Kariwala tuning rule was also observed to be superior in terms of the settling time and overshoot performance measure in the liquid level control. Similarly, the optimization algorithms-based controller gave better performance in terms of IAE and other time domain specifications while controlling the pressure process station. The limitation of the proposed tool, it is not suitable for experimenting with the unstable processes and the multi input-multi output processes. It was also assumed that the process behavior could be adequately described by a FOPTD model. In the future, the proposed tool could be enhanced by adding other optimization algorithms, frequency domain analysis and multi-input multi-output process controlling ability. The facility could be added in the proposed tool to study the effect of optimization algorithm's random parameters on the process output. The proposed tool \"RTDA_Toolbox\" could be downloaded from the link in [[https://drive.google.com/open?id=14](https://drive.google.com/open?id=14) GcTMwft7 pn3 bLcxOTvkTX_MNIDdIBLu]([https://drive.google.com/open?id=14](https://drive.google.com/open?id=14) GcTMwft7 pn3 bLcxOTvkTX_MNIDdIBLu). [PERSON] proposed the simulation tool concept and implemented the model order reduction and tuning of controller for different processes and wrote the paper; [PERSON] did the controller performance analysis and [PERSON] proposed the optimal tuning and edited the paper. This research was funded by VIT, Vellore.