## 4 Tool Description  
The FOPDT process model is required for the RTD-A controller design. If the process transfer function is not in the FOPDT form, then the model order reduction tool (Tool-I) will be used. The higher order process dynamics can be represented by the reduced model. The process reaction curve (PRC) method is one of the techniques to reduce the higher order process model into the first order model with dead time. It is considered to achieve a minimum integral squared error (ISE) in its ideal open loop step response. The GUI to reduce the higher order transfer function into the first order model is shown in Figure 3. Tool-I has the ability to reduce the higher order transfer function into the first order transfer function \(G(s)\). The proposed Tool-I has experimented on the liquid level process and it is discussed in the evaluation section [4]. The outline of the method of model order reduction is given in (32). \[Reduced\ Order\ Model\ G(s)=method\bigg{\{}\frac{a(s)}{b(s)}\bigg{\}}, \tag{32}\]  
where \(a(s)\) and \(b(s)\) is the numerator and denominator polynomial of the higher order process transfer function respectively and \(G(s)\) is the required reduced order model with or without dead time [4]. Generally, the system has some dead time when it is tested for robustness in a real-time environment. The Tool-I has the option to add a time delay while testing the system. Each order reduction method takes the dead time as one of the input and provides the required reduced order model output. Since the RTD-A controller depends on the process gain, process time constant and dead time, Tool-I basically separates all three parameters. It has also the facility with three model order reduction methods such as the Skogestad, two-point and fraction incomplete method. The chosen methods are effective for an over-damped process with dead time. Figure 3: Graphical user interface (GUI) for the model order reduction tool. Next, this paper introduced the second tool that was designed for calculating the RTD-A controller tuning parameters. The GUI for the Tool-II is shown in Figure 4. This Tool-II was designed by considering the first order process with dead time models. It has the flexibility to tune the controller with both Ogunnaike and Kariwala tuning rules. The developed GUI has the provision for getting different inputs such as the sampling time (\(dt\)), process uncertainty parameter (\(\lambda\)), the process dead time (\(\alpha\)), composite noise factor (\(f_{n}\)) and process time constant (\(\tau\)) from the user. The user can choose one tuning rule from the selection menu. The calculated, tuning values will be shown in the RTD-A parameter portion of the Tool-II. If the user selects another tuning method from the menu during the next stint, the user has the provision to compare both results just by enabling the \"Tune\" button in operating the menu. Tool-II also has the facility to reset all the values to zero [20]. The tuning rules used in Tool-II are presented in Tables 1 and 2. In order to understand the effect of each controller parameter (\(\theta_{R}\), \(\theta_{T}\), \(\theta_{D}\) and \(\theta_{A}\)) on the process output the user has to change the value manually. The tool is made in such a way that the researcher has the facility to tune the controller manually with a slider option. The representation of Tool-III from the proposed tool is shown in Figure 5. The designed Tool-III has the facility to give a different set point at a different time instant and the user can perform a time domain analysis from the step response of the process. The disturbance can be added at a required instant and the user can check the effect of a tuning parameter on the process output with disturbance. Tool-III has also the provision to save current values of tuning parameters. After giving a set point to the tool, the user can change the values of the tuning parameters and check the tuning values in the slider display. The researcher can visually check the controller output and closeness to the ideal values.