## 1 Introduction  
The proportional integral and derivative (PID) controllers are mostly used to control the critical processes in all over the industries. Most of the industrial closed loop control schemes use the PID controller [1]. Implementing a PID controller is an easy task by considering its simple structure [2]. The process becomes complex by adding a disturbance signal with uncertainty process parameters. The PID controller depends on technology, which is inefficient to fulfill the need of the 21 st century. In general, classical control schemes have been positively used only on well-defined plant models [3]. The PID controller has three tuning parameters, which are the proportional gain (\(K_{P}\)), integral gain (\(K_{I}\)) and derivative gain (\(K_{D}\)) [4, 5]. The PID controller parameters are difficult to tune in achieving more robust performance with the existence of process uncertainties [6]. Even after choosing the tuning values user will be facing difficulties in achieving the disturbance rejection and set point tracking simultaneously within the restricted time. Most of the industrial processes need a controller with the combo of set point tracking and disturbance rejection ability, which is challenging with the classic PID controller. One may get some unsatisfactory results using tuning rules such as Ziegler-Nichols (ZN) and Cohen-Coon (CC) [7]. It may be debated that contemporary optimization techniques have made the classical tuning methods needless. These techniques mandate a reliable plant model, which is to be controlled effectively [8]. The user can solve the problem of optimization by considering some advanced version of control scheme such as the model predictive controller (MPC). MPC can be effective in the typical industry situations, but still, they have certain limitations, which are generally not mentioned in advance process control applications. The limitations are difficulty in industry operations, high maintenance cost and less flexibility towards industrial functioning. All the mentioned limits may lead to insubstantial controllers that are not cost-effective. There are many discrepancies while calculating the tuning parameters [9]. The Smith predictor algorithm can be implemented across the controller transfer function for reducing the effect of dead time on the process output. Internal model control (IMC) has the advantage of robust tuning rules, but it has the disadvantage of a large process time constant. In the IMC scheme the user needs to choose integral time equal to the process time constant [10]. Therefore, it is inevitable to design a novel alternate control scheme that has all the combined advantages of PID and MPC. In 2005, [PERSON] and [PERSON] had introduced a new control algorithm RTD-A, which is more efficient than PID for chemical and other industrial processes [11]. The RTD-A controller is introduced to rectify all the disadvantages of classical controllers such as PID, IMC and MPC. This controller tuning is based on the characteristics such as robustness, tracking, disturbance rejection and aggressiveness of the controller, which is prefixed as RTD-A controller. The RTD-A controller can be implemented only in discrete time. The performance of the controller is greatly influenced by the sampling time and therefore a careful selection of sampling time is inevitable. The clear relationship between the RTD-A controller tuning parameters and characteristics is very well explained and also robust stability of the proposed regulatory controller investigated by [PERSON] and [PERSON] [12; 13]. In 2011 [PERSON] introduced the block diagram representation of RTD-A controller implementation and an alternate tuning rule. The rules are focused on optimizing the results, but the [PERSON] algorithm was working better than the Ogunnaike rules [14]. In the past, there are tools such as the fractional order modeling and control (FOMCON), Jonathan model predictive controller (jMPC) and N-integer. They could be used for real-time applications.