Comparison of fractional and integer PID controllers tuned by genetic algorithm

Castro, Felipe A.; Bernardes, Nemerson D.; Cuadros, Marco Antônio de Souza Leite; Almeida, Gustavo Maia de

doi:10.1109/induscon.2016.7874592

Cited by 13 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, the authors of the conference paper in [21] applied both IOPID and FOPID controllers to a liquid-level control problem while tuning both using genetic programming. The results show that "... the PI λ D µ has performed slightly better for the response signal ...".…”

Section: A Growing Trend From Classical Pid Controllers To Fopid mentioning

confidence: 99%

Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments

et al. 2021

View full text Add to dashboard Cite

The interest in fractional-order (FO) control can be traced back to the late nineteenth century. The growing tendency towards using fractional-order proportional-integral-derivative (FOPID) control has been fueled mainly by the fact that these controllers have additional "tuning knobs" that allow coherent adjustment of the dynamics of control systems. For instance, in certain cases, the capacity for additional frequency response shaping gives rise to the generation of control laws that lead to superior performance of control loops. These fractional-order control laws may allow fulfilling intricate control performance requirements that are otherwise not in the span of conventional integer-order control systems. However, there are underpinning points that are rarely addressed in the literature: (1) What are the particular advantages (in concrete figures) of FOPID controllers versus conventional, integer-order (IO) PID controllers in light of the complexities arising in the implementation of the former? (2) For real-time implementation of FOPID controllers, approximations are used that are indeed equivalent to high-order linear controllers. What, then, is the benefit of using FOPID controllers? Finally, (3) What advantages are to be had from having a near-ideal fractional-order behavior in control practice? In the present paper, we attempt to address these issues by reviewing a large portion of relevant publications in the fastgrowing FO control literature, outline the milestones and drawbacks, and present future perspectives for industrialization of fractional-order control. Furthermore, we comment on FOPID controller tuning methods from the perspective of seeking globally optimal tuning parameter sets and how this approach can benefit designers of industrial FOPID control. We also review some CACSD (computer-aided control system design) software toolboxes used for the design and implementation of FOPID controllers. Finally, we draw conclusions and formulate suggestions for future research.

show abstract

Section: A Growing Trend From Classical Pid Controllers To Fopid mentioning

confidence: 99%

Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Work [10] completes that the PID controller has a simple control structure, reduced power consumption, and satisfactory efficiency. Also, approximately 90 % of industrial controllers still are currently PIDs [11]. The author from work [12] stated that the use of the PID controller causes the insertion of a pole into the system where this additional pole usually assists on limiting the action or gain, of the controller at high frequency.…”

Section: System Modellingmentioning

confidence: 99%

Study of Non-Linear Systems: PI and Fuzzy Controllers Performances

Machado¹,

Santos²,

Carvalho³

et al. 2020

Wseas Transactions on Power Systems

View full text Add to dashboard Cite

This work aims to present the development and study of a nonlinear system structure capable of moving in one degree of freedom, performed by a brushless motor coupled to a propeller. For this, the mathematical modeling of the developed structure was based on resembling a simple physical pendulum. Regarding the system control, two techniques were considered: PID and Fuzzy, implemented in Matlab. Simulation tests were carried out to validate and compare the control technique results with different input signals. Both controller topologies tracked the respective setpoints, but Fuzzy controller had the best system performance. The structure developed can serve as a base platform for future improvements, studies, and experiment tests, such as the analysis of motion in the horizontal plane.

show abstract

“…Mohamed et al [23] used genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to optimize PID controller and applied it in the control system of shred DC motor to improve the performance of the system. In the above research, the characteristics of multi-objective optimization of machine learning algorithm and the advantages of overcoming the sensitivity of initial parameter values of simplex method were utilized to optimize controller parameters [24,25]. In this paper, machine learning algorithms [26] are used to optimize the controller parameters, improve the robustness of the control system, increase the control accuracy, and suppress the drill string stick-slip vibration to the greatest extent.…”

Section: Introductionsmentioning

confidence: 99%

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

Su,

Wei,

et al. 2023

Sound&Vibration

View full text Add to dashboard Cite

During the drilling process, stick-slip vibration of the drill string is mainly caused by the nonlinear friction generated by the contact between the drill bit and the rock. To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations, the Fractional-Order Proportional-Integral-Derivative (FOPID) controller is used to suppress stick-slip vibrations in the drill string. Although the FOPID controller can effectively suppress the drill string stick-slip vibration, its structure is flexible and parameter setting is complicated, so it needs to use the corresponding machine learning algorithm for parameter optimization. Based on the principle of torsional vibration, a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed. The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model. The adaptive learning strategy of genetic algorithm (GA), particle swarm optimization (PSO) and particle swarm optimization improved (IPSO) by arithmetic optimization (AOA) is used to optimize and adjust the controller parameters, and the drill string stick-slip vibration is suppressed to the greatest extent. The results show that: When slight drill string stick-slip vibration occurs, the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration. However, the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration (stuck pipe) out of trouble, and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model. Set a reasonable range of drilling parameters (weight on bit/drive torque) in advance to avoid severe stick-slip vibration (stuck pipe) in the drill string system. KEYWORDSStick-slip vibration; machine learning; fractional order proportional integral derivative (FOPID) control; optimization algorithm Nomenclature C pb Drill bit viscous damping [(N • m • s)/rad] C rs Viscous damping coefficient of the turn table [(N • m • s)/rad] J zp , J zg , J BH , J zt Rotational inertias of the turn table, drill pipe, BHA, and drill bit, respectively [kg • m 2 ] K Pg , K gB , K Bt Torsional stiffness of the springs between the turn table and drill pipe, drill pipe and BHA, and BHA and drill bit, respectively [(N • m)/rad] N pg , N gB , N Bt Torsional damping of the springs between the turn table and drill pipe, drill pipe and BHA, and BHA and drill bit, respectively [(N • m • s)/rad]

show abstract

Comparison of fractional and integer PID controllers tuned by genetic algorithm

Cited by 13 publications

References 3 publications

Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments

Towards Industrialization of FOPID Controllers: A Survey on Milestones of Fractional-Order Control and Pathways for Future Developments

Study of Non-Linear Systems: PI and Fuzzy Controllers Performances

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

Contact Info

Product

Resources

About