Study on PID controller design and performance based on tuning techniques

Rao, P. Krishna; Subramanyam, M. V.; Satyaprasad, K.

doi:10.1109/iccicct.2014.6993183

Cited by 28 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fundamental methods such as Ziegler-Nichols, Cohen-Coon, Chien-Hrones-Reswick, and Aström-Hagglund are used to adjust these parameters. 14 In addition, hybrid methods such as fuzzy-tuned PID controllers 15 and optimization algorithms can be used to adjust these parameters. Various optimization algorithms, such as the ABC optimization algorithm, 16 gray wolf optimization algorithm, 17 evolutionary optimization algorithms, 18 particle swarm optimization, 19 Harris Hawks optimization algorithm, 20 and Harris Hawks gas optimization algorithm, 21 can be used for parameter tuning.…”

Section: Literature Reviewmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

View full text Add to dashboard Cite

Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…where K P , K I , and K D are the proportional, integral, and derivative gains, respectively; m is the manipulation signal; and e is the error signal [43].…”

Section: Pidmentioning

confidence: 99%

Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning

Portillo,

Garza-Castañón,

Minchala-Avila

et al. 2023

Machines

View full text Add to dashboard Cite

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.

show abstract

“…The PID tunning method usually use Ziegler Nichols [7], [9], but in this study using Root Locus, because the others method does not meet the criteria. Root Locus is an analysis that describes shift of the closed loop poles position from change in the magnitude of open loop with gain adjustment [10].…”

Section: Determination Of Pid Control Parametersmentioning

confidence: 99%

Real-time Monitoring System Using LabView for DC Motor Position Control Embedded System with PID and Pole Placement Control

Handaya¹,

Fauziah²,

Listyorini³

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

View full text Add to dashboard Cite

This study builds a real-time monitoring system for positioning DC motors using LabView. Control parameters are obtained from DC motor modeling using Matlab Toolbox Identification. The DC motor rotation position control method chosen in this research is PID and Pole Placement-Full Order State Feedback. The experiments were performed by testing the tracking results on each of the controls and real time monitoring on LabView. Data acquisition and computer systems, utilizing LabView's VISA protocol based on Arduino Mega embedded system with data logger. Based on the experiments, LabView can be used as a real-time monitoring system in controlling the rotation of DC motor position using PID and Pole Placement.

show abstract

Study on PID controller design and performance based on tuning techniques

Cited by 28 publications

References 13 publications

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning

Real-time Monitoring System Using LabView for DC Motor Position Control Embedded System with PID and Pole Placement Control

Contact Info

Product

Resources

About