Fractional-Order Predictive PI Controller for Dead-Time Processes With Set-Point and Noise Filtering

Abstract

In most of the industrial process plants, PI/PID controllers have been widely used because of its simple design, easy tuning, and operational advantages. However, the performance of these controllers degrades for the processes with long dead-time and variation in set-point. Up next, a PPI controller is designed based on the Smith predictor to handle dead-time processes by compensation technique, but it failed to achieve adequate performance in the presence of external noise, large disturbances, and higher-order systems. Furthermore, the model-based controllers structure is complex in nature and requires the exact model of the process with more tunable parameters. Therefore, in this research, a fractional-order predictive PI controller has been proposed for dead-time processes with added filtering abilities. The controller uses the dead-time compensation characteristics of the Smith predictor and the fractional-order controller's robustness nature. For the high peak overshoot, external noise, and disturbance problems, a new set-point and noise filtering technique is proposed, and later it is compared with different conventional methods. In servo and regulatory operations, the proposed controller and filtering techniques produced optimal performance. Multiple real-time industrial process models are simulated with long dead-time to evaluate the proposed technique's flexibility, set-point tracking, disturbance rejection, signal smoothing, and deadtime compensation capabilities.

Fractional-order PI plus D controller for second-order integrating plants: Stabilization and tuning method

“…Therefore, the CRB is utilized to construct a stability boundary locus in which a stability domain of the closed loop system is identified. As mention earlier, the CRB is constructed when jω is substituted for s. Therefore, jω is substituted for s in (21) and the following expression defined in ( 22) can be obtained…”

“…There are several combinations in which this controller could be implemented where PID, PI, PD, PI-PD and I-PD are some examples [8][9][10][11][12][13]. The past few years have had substantial focus on fractional calculus for controls, thus, increasing its popularity in the field of control theory [14][15][16][17][18][19][20][21][22][23][24]. In contrast to integer order, fractional-order PID (FOPID) controllers introduce two more tuning parameters, commonly denoted as λ and µ, which are the powers of integrator and derivative terms in the PID controller, respectively.…”

A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control

“…Figure 8 shows a comparison of the IAE values for different controllers in the WirelessHART network for various industrial processes. Meanwhile, the tunable parameters possessed by the different controllers discussed above are shown in Table 6 [ 70 ].…”

Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems

“…The use of centralized management allows for network control as well as the simplification of node hardware and software. Wireless standards such as ISA SP100.11a, WirelessHART (WH), and WIA-PA standards are widely implemented in IWSN applications [5,7]. These standards typically form mesh networks, with nodes acting as routers to improve communication path availability [8].…”