# Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID
###### Abstract  
In this research, a control mechanism for fuzzy fractional-order proportional integral derivatives was suggested (FFOPID). The fractional calculus application has been used in different fields of engineering and science and showed to be improved in the past few years. However, there are few studies on the implementation of the fuzzy fractional-order controller for control in real time. Therefore, for an experimental pressure control model, a fractional order PID controller with intelligent fuzzy tuning was constructed and its results were calculated through simulation. To highlight proposed control scheme advantages, the performances of the controller were inspected under load disturbances and variations in set-point conditions. Furthermore, with classical PID control schemes and fractional order proportional integral derivative (FOPID), a comparative study was made. It is revealed from the results that the suggested control scheme outclasses other categories of the control schemes. fractional-order control; pressure control; PID controller; pressure system identification; fuzzy logic control +
Footnote †: journal: Applied
proportional (KP) constants are present in the fractional-order PID controller, along with two additional constants, a differentiator of order \(\mu\) and an integrator of order \(\lambda\)[16; 17]. According to recent research, the FO control scheme provides an improved substitute to traditional integer-order control for control applications in industrial in terms of precision and robustness [18; 19; 20; 21; 22]. [PERSON] et al. [23] presented a fractional fuzzy PID control algorithm for common industrial temperature control systems to improve the production quality and accuracy of control models. A basic system of fractional orders is used to describe the temperature control process. The temperature profile controlled by the proposed algorithm can achieve more satisfactory dynamic performance and better robustness to environmental changes caused by internal and external disturbances. Most of the industrial processes are complex and have strong nonlinearity. Hence, it is challenging to develop an accurate mathematical model to design a classical controller and obtain satisfactory control performance. In order to overcome these issues, a self-tuning and adaptive mechanism is necessary for classical controllers. The fuzzy set theory gives maximum flexibility in developing intricate applied control methods. In fuzzy set-theory, to express the observations, easier linguistic notations are used. The Fuzzy Logic turned the design of controller to be simple and yet for more complicated and non-linear industrial process without using the particular mathematical representation of the system [24]. Additionally, the plant parameters are varying [25; 26]. The classical controller is combined with FLC for guarantee optimum performance and fine-tuning parametric gains owing to non-linearities and load turbulences. [PERSON] and [PERSON] [27] proposed a study to control the liquid level of Two Tank Spherical Interacting System where they posited Fuzzy Fractional Order Proportional Integral Derivative (FFOPID) controller. Experimental outcomes prove that FFOPID is superior to all other existing methods in terms of various time domain specifications and time integral performance measurements. In power plants, chemical industries, well drilling, and automobiles pressure control is vital. In industries, the pressure plants usually intersect and operates at various pressure levels. Moreover, these pressure plants are often exposed to the rapid variations in the load. Hence, there is a requirement for an effective control scheme to avoid the difficultly of free operation. In earlier days, industries used to regulate the pressure through traditional control techniques, for example, proportional integral (PI) or proportional integral derivative (PID). [PERSON] et al.