# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 3 Industrial Applications of WirelessHART
### Real-Time Implementation  
[PERSON] et al. [64] initiated research on applying WirelessHART in real-time industrial process control and demonstrated their results. The researchers used a modified Freescale 1321 xEvk toolkit written in the ANSI C language and created a super-frame time slot configuration for the hosted devices. Simple data scheduling and transmission between the field devices were carried out to indicate the possibility of monitoring and data transfer using the WirelessHART Network. In [65], multiple control strategies for WirelessHART network devices are proposed--namely, (1) controlling through the host, which supports complete control; (2) controlling through the field, which supports partial control; and (3) controlling through the gateway, which supports full control and has less latency compared to all the other approaches. They used the WirelessHART temperature transmitter to transmit and acknowledge the data transmission through the gateway without controlling the process. Real-time experimentation on the distillation column pressure and steam flow was conducted to prove that control over WirelessHART is possible [66]. The process was controlled using a conventional PID controller in both cases to maintain the process set-point. The results proved that WirelessHART transmitters improved the accuracy and performed as reliably as the wired communication without signal filters. Additionally, wireless transmitters are not affected by ground loops, which often affects wired field devices. LabVIEW-based WirelessHART experimentation was conducted to study the effect of packet loss on the network control system stability [67]. Multiple industrial communication protocols were compared regarding the gradual increase in the loss probability of data packets from 0 to 100% to understand their impacts on a network control system's stability. The PIDPlus algorithm, an improved variant of the conventional PID controller, was employed to take care of slow process updates, packet loss in the communication channel, and non-periodic measurement updates encountered by WirelessHART transmitters [68]. Two other studies using a PID controller with a Kalman filter and Smith predictor were conducted to compare it with the PIDPlus algorithm. The PID with the Kalman filter had a better performance in terms of integral absolute error (IAE). Developments for monitoring and controlling the dividing wall column using event-based model predictive control (MPC) were carried out in [69]. MPC outperformed the PID controller, with faster handling and better compensation for the external disturbances. It suffered from network-induced delay and the implementation complexity increased due to the greater number of tunable parameters. Experimentation with an internal model control (IMC) aimed to investigate the disturbance rejection and set-point tracking capabilities in an industrial-scale pilot process plant. IMC showed a better performance in set-point tracking and overshoot reduction than the PID controller, but when the system order increased the IMC had a slower rise time and increased peak overshoot, which caused the system to settle as equal PID. In [14], the [PERSON] predictor-based filtered predictive PI (FPPI) controller was analyzed in an attempt to compensate for the model mismatch and high-frequency noise issues faced by IMC. Additionally, this controller possessed useful time-delay prediction capabilities to handle stochastic systems. This controller has a simple design structure and has the same number of tunable parameters as the conventional PID controller, which led to its easy implementation and robust performance. Figure 8 shows a comparison of the IAE values