Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator

Ardeshiri, Reza Rouhi; Kashani, Hoda Nikkhah; Ahrabi, Atikeh Reza

doi:10.1504/ijaac.2019.101912

Cited by 9 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the performance of this fractional-order PID is further enhanced using intelligent fuzzy techniques to achieve better servo and regulatory responses. Therefore, various combinations of fractionalorder PID and fuzzy logic are proposed in the literature to form fractional-order fuzzy PID controller for two-link [4,39,43,44,51,62,63,67,79,92,95], three-link manipulators [48,65], SCARA [31,53], PUMA 560 [30], and Stewart platforms [89]. In addition, the authors of [64] have proposed a hybrid two-degree-of-freedom fractional-order fuzzy PID controller by combining two-degree-of-freedom PID, fractional-order concept, and fuzzy logic.…”

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

“…Further, to incorporate the self-tuning of controller parameters rather than designing using precise mathematics, researchers have used several optimization techniques where the non-linear controller gains are updated in real-time using error and fractional rate of error. The optimization techniques used in the literature are artificial bee colony [39,43,53,95], genetic algorithm [30,39,43,64,79], cuckoo search [4,31,48,51,63], backtracking search [44,65], dragonfly [79], ant lion optimizer [79], particle swarm optimization [67,89] and grey wolf optimizer [95]. The robustness testing of these self-tuned fractional-order fuzzy PID controllers has shown superior tracking results in comparison to the conventional counterparts.…”

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

2023

View full text Add to dashboard Cite

Robot manipulators are widely used in many fields and play a vital role in the assembly, maintenance, and servicing of future complex in-orbit infrastructures. They are also helpful in areas where it is undesirable for humans to go, for instance, during undersea exploration, in radioactive surroundings, and other hazardous places. Robotic manipulators are highly coupled and non-linear multivariable mechanical systems designed to perform one of these specific tasks. Further, the time-varying constraints and uncertainties of robotic manipulators will adversely affect the characteristics and response of these systems. Therefore, these systems require effective modelling and robust controllers to handle such complexities, which is challenging for control engineers. To solve this problem, many researchers have used the fractional-order concept in the modelling and control of robotic manipulators; yet it remains a challenge. This review paper presents comprehensive and significant research on state-of-the-art fractional-order modelling and control strategies for robotic manipulators. It also aims to provide a control engineering community for better understanding and up-to-date knowledge of fractional-order modelling, control trends, and future directions. The main table summarises around 95 works closely related to the mentioned issue. Key areas focused on include modelling, fractional-order modelling type, model order, fractional-order control, controller parameters, comparison controllers, tuning techniques, objective function, fractional-order definitions and approximation techniques, simulation tools and validation type. Trends for existing research have been broadly studied and depicted graphically. Further, future perspective and research gaps have also been discussed comprehensively.

show abstract

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

2023

View full text Add to dashboard Cite

show abstract

“…The simulation results show that the proposed approach works well considering disturbance changes and parameter uncertainties. A fuzzy FO-PID is used in [64] to control the position of a robotic manipulator. A fuzzy system combined with the particle swarm optimization method is used to determine the parameters of a FO-PID controller.…”

Section: Applications and Self-tuned Fo-pidsmentioning

confidence: 99%

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

et al. 2022

View full text Add to dashboard Cite

The scientific community has recently seen a fast-growing number of publications tackling the topic of fractional-order controllers in general, with a focus on the fractional order PID. Several versions of this controller have been proposed, including different tuning methods and implementation possibilities. Quite a few recent papers discuss the practical use of such controllers. However, the industrial acceptance of these controllers is still far from being reached. Autotuning methods for such fractional order PIDs could possibly make them more appealing to industrial applications, as well. In this paper, the current autotuning methods for fractional order PIDs are reviewed. The focus is on the most recent findings. A comparison between several autotuning approaches is considered for various types of processes. Numerical examples are given to highlight the practicality of the methods that could be extended to simple industrial processes.

show abstract

“…In the numerical implementation of a Fractional Order Control, the most important step is the numerical evaluation of the discretization of the fractional-order derivations S α . The fractional-order ordering system is characterized by the following transfer function [25,26] Hence the discrete transfer function of the fractional-order system G (z) can be obtained as: (14) w(z -1 ), present the discrete equivalent of the Laplace operator S. To implement the fractional-order model in the control object of this work, we will use the singularity function method developed by Charef et al [27] and Bai et al [28]. For a fractional system of the first order, the approximation method targets the slope of 20 mdB/dec on the Bode plot of the PPF by the number of lines in the form of a zigzag, produced by an alternation of 20dB/dec and 0 dB/Dec such as: p0<z0<p1<z1<.…”

Section: The Pi Fractional Controller Of the Smr8/6mentioning

confidence: 99%

Smart Controls for Switched Reluctance Motor 8/6 Used for Electric Vehicles Underground Mining Security

Rechach¹,

Ghoudelbourk²,

Zoubir³

et al. 2021

EJEE

View full text Add to dashboard Cite

Switched reluctance motors (SRM) are a type of electromagnetic machine that has piqued the interest of manufacturers, as opposed to induction, brushless, or permanent magnet machines. This is because the rotor is simple, robust, and lacks coils, windings, and permanent magnets. It can also operate in a wide range of power in the electric vehicle's drive, even in extreme conditions such as underground mines, ensuring a longer life of service. However, due to the toothed shape of the rotor, the SRM is characterized by vibration and acoustic noise. To solve this problem to better adapt the SRM to the electric vehicle, we propose to use intelligent techniques such as the controller (ANN) and the fractional order controller (PI α ). This article compares two intelligent speed controllers that use direct torque control (DTC) to reduce torque ripples. As a result, when associated with direct torque control, the Fractional Order Controller (PI α ) outperforms the Artificial Neural Network (ANN).

show abstract

Design and simulation of self-tuning fractional order fuzzy PID controller for robotic manipulator

Cited by 9 publications

References 0 publications

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

Smart Controls for Switched Reluctance Motor 8/6 Used for Electric Vehicles Underground Mining Security

Contact Info

Product

Resources

About