Optimal Setting and Control Strategy for Industrial Process Based on Discrete-Time Fractional-Order PI$^{\lambda}$ D$^{\mu}$

Zhang, Fengxue; Yang, Chunhua; Zhou, Xiaojun; Gui, Weihua

doi:10.1109/access.2019.2909816

Cited by 21 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, any K p and K i in this stability region for the plant would be reliable. Now, the ITAE is commonly referred to as a good performance index in designing PID controllers [13]. The ITAE function is given by…”

Section: A Example 1 : Twin Rotor Mimo Systemmentioning

confidence: 99%

Fractional PI Controller for Integrating Plants

Azid

Shankaran

Mehta

2020

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)

View full text Add to dashboard Cite

This paper verifies a fractional-order PI controller to stabilize integrating time delay plants. Firstly, the stability region by non-integer order controller namely, P I λ is illustrated using boundary condition. This is achieved using complex root boundary domains. The controller parameters are calculated using the ITAE criterion for integrating plant with time delay. The twin rotor MIMO system (TRMS) as an integrating plant is verified in our study. Another example with large time delay is studied to check the validity of fractional-order integrator. Both simulation and experimental results are discussed and compared by P I λ with classical P I. The result proves the effectiveness of the fractional order controller.

show abstract

Section: A Example 1 : Twin Rotor Mimo Systemmentioning

confidence: 99%

Fractional PI Controller for Integrating Plants

Azid

Shankaran

Mehta

2020

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)

View full text Add to dashboard Cite

show abstract

“…In the field of control engineering, PID (Proportional-Integral-Derivative) controller is a classical feedback control algorithm. It is widely used in industrial control systems [1][2][3][4][5][6][7].The PID controller regulates the output of the system by means of three components: proportional, integral and differential. to achieve precise control of the target value.…”

Section: Introductionmentioning

confidence: 99%

A Control Method for Improved Fuzzy PID of GSOM for Fish Scale Evolution

Wang,

Cui,

Sui

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Fuzzy PID control is a control method with good adaptability and stability in complex environments. It is used to achieve precise regulation and stable control of the system. In this paper, a fish scale evolution GSOM is proposed to improve the control method of fuzzy PID. Firstly, the fish scale regulation system is established and the differential evolution theory is introduced to realize the evolutionary upgrading of the system. Secondly, the GSOM module is introduced. The system is optimized by self-organized mapping neural network to achieve dynamic regulation of polymorphic inputs. Meanwhile, the fuzzy rule base and parameter regulation mechanism in fuzzy PID control are dynamically optimized. Improve the performance of the control system. Finally, the control method of improved fuzzy PID for fish scale evolution GSOM is simulated using MATLAB. The simulation experiments also compare several traditional PID control methods. The comparison indexes include stability, robustness, control accuracy and feedback output effect. The results show that the method in this paper is more stable and has fewer iterations when facing the dynamic input environment. The tracking error and control output of the controller system are significantly improved. It has good feedback output effect, solves the saturation problem and has higher control accuracy.

show abstract

“…After this first development, the use of the FOPID controller to regulate different types of systems increased. Examples of the applications in engineering systems that are regulated by FOPIDs are: smart reactors [17], electronic power converters [18][19][20], rehabilitation systems [21]; automatic voltage regulators [22,23], industrial process models [24,25], robotic systems [26], power systems using synchronous generators [27], and wind turbines [28].…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Fractional PID Controllers Applied to a Two-Tank System

et al. 2023

View full text Add to dashboard Cite

An experimental validation of fractional-order PID (FOPID) controllers, which were applied to a two coupled tanks system, is presented in this article. Two FOPID controllers, a continuous FOPID (cFOPID) and a discrete FOPID (dFOPID), were implemented in real-time. The gains tuning process was accomplished by applying genetic algorithms while considering the cost function with respect to the tracking error and control effort. The gains optimization process was performed directly to the two-tanks non-linear model. The real-time implementation used a National Instruments PCIe-6321 card as a data acquisition system; for the interface, we used a Simulink Matlab and Simulink Desktop Real-Time Toolbox. The performance of the fractional controllers was compared with the performance of classical PID controllers.

show abstract

Optimal Setting and Control Strategy for Industrial Process Based on Discrete-Time Fractional-Order PI$^{\lambda}$ D$^{\mu}$

Cited by 21 publications

References 35 publications

Fractional PI Controller for Integrating Plants

Fractional PI Controller for Integrating Plants

A Control Method for Improved Fuzzy PID of GSOM for Fish Scale Evolution

Experimental Validation of Fractional PID Controllers Applied to a Two-Tank System

Contact Info

Product

Resources

About