Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

Karam, Zeyad A.; Ghintab, Shahad S.; Hasan, Sami

doi:10.21533/pen.v7i2.548

Cited by 4 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A typical type-2 fuzzy membership function consists of two membership functions of the type-1 fuzzy system, where the first one is the "upper membership function" (UMF) and "lower membership functions" (LMF). The mathematical representation for FOU can be described by the union between LMF and UMF, the footprint can be described as X [20,21].…”

Section: 2control Methodologymentioning

confidence: 99%

“…The type-1 and type-2 fuzzy logic system architecture contain the same mechanism, but with a substitute defuzzifier at the output processor [20]. The IT2FLC diagram may consist of [21]; Fuzzifier Where the crisps inputs vector ( ) is entered fuzzification process under the fuzzified block where it is structured into type-2 fuzzy sets ̃.…”

Section: 2control Methodologymentioning

confidence: 99%

“…The joint friction consists of Coulomb, Viscous, and Stribeck frictions are specified in Equation 21: (21) Where F represents the Forces vector of the friction.…”

Section: Figure 1 Scara Arm With Two Redundant Degreesmentioning

confidence: 99%

See 2 more Smart Citations

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

2022

IJATEE

View full text Add to dashboard Cite

To meet the COVID-19 challenges, the fifth digital industrial wave demands an artificially-intelligent control system advances in selective compliance assembly robot arm (SCARA). Since SCARA has an inherently nonlinear dynamic system and friction rejection, this work develops an artificial intelligent (AI) fuzzy-based nonlinear control algorithm (impedance strategy). Consequently, a generalized dynamic five degree of freedom SCARA (5-Dof SCARA) has mathematically been modelled. These nonlinear controllers have been realized as AI-based fuzzy architectures for the position part from the impedance controller; fuzzy logic proportional derivative (PD) control-type one and Fuzzy logic proportional derivative integral (PID) control type two. These AI architectures regulate the position tracking error of the end effectors' forces. That has been tested using procedures based on half-elliptic and full-elliptic trajectories. Comparatively, the test results are tabulated with related existing published results. Both AI-based controllers have been efficiently dealt with robot nonlinear models and friction rejection. However, the proposed Fuzzy logic PD type one controller has been produced a less-optimum response with the inherent system nonlinearities. Thus, an AI fuzzy-based control algorithm has to be optimally developed to resolve the SCARA sluggishness in achieving tasks. Consequently, a fuzzy logic control (FLC) algorithm of a three-dimension membership function (fuzzy type-2) has been designed to improve the position response of the SCARA redundant robot end-effectors; Fuzzy logic PID type two-controller. The third dimension is dedicated to overcoming uncertain limits in the nonlinear system that leads to a more stable response in the robot end-effector with no oscillation and zero error. The test results of the AI fuzzy-based PID nonlinearcontroller has been successfully manifested the superiority in performance to be properly updated in the space shuttle remote manipulator system. Where the large enchantment in position trajectory reached by FLC type-1 PD controller is 93.166% and 25% in X and Y axis respectively, and the large enhancement by the FLC type-2 PID controller is 95.501% and 31.250% in X and Y-axis respectively.

show abstract

Section: 2control Methodologymentioning

confidence: 99%

Section: 2control Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

2022

IJATEE

View full text Add to dashboard Cite

show abstract

“…PS, PM PB, where P is Positive, B is Big. The designed Simulation modelled by MATLAB, which involves the PD in FLC structure as the controller for the 2 DoFs assisting arm [23] and [24]. The designed robot will be used for controlling the real-time implemented robot via the designed interfacing circuits.…”

Section: Fuzzy Controller Design and Simula-tion Of The Robot Kinematicsmentioning

confidence: 99%

Design, Implementation, Interfacing and Control of Internet of Robot Things for Assisting Robot

Karam¹,

Abdulkadhim²

2022

IJCDS

Self Cite

View full text Add to dashboard Cite

Robotic arm plays a great role in assisting the human hand in current life. This project involves the design and implementation of two degrees of freedom (DOF's) assistant arm robot for aiding humans in the medical recovery for the upper left limb motion. The contribution in this work involves three parts: the first one, design special interfacing circuits based on (Arduino card) for controlling the designed arm motion, where the motion execution and record is done by designed reading and writing circuits, these circuits controlled by a designed MATLAB fuzzy proportional derivative (PD) controller, this control structure implemented by assisting of special Arduino Simulink library In MATLAB Simulink, where it will be used for control the signals transition, a new Simulink design proposed and investigated to interfacing the controller with the arm motors and encoders via the circuits. The second contribution is adding an assisting spring system to the arm first motor joint, where this addition rises the motion stability through the control phase by increasing the required torque in the arm first joint, especially after the human hand load is attached to the robot links, where its tested effectively without needing for replacing the motor with increased torque. The results illustrate the power of these circuits design and the error reduction occurred after supporting the system's spring. The Third contribution, An Internet of robotic thing (IoRT) model is designed, and then the robot arm is connected using our designed internet -based control system using IoT protocols for communication.

show abstract

“…Hence, a more sophisticated approach is needed to refine the controller parameters to produce an optimum reaction. [33] Two evolutionary algorithms are examined to optimize the PID controller, the PSO, and the WOA. When the PSO algorithm is used with the parameters of Table 2.…”

Section: Conventional Pid Tuned By Pso Algorithmmentioning

confidence: 99%

Design of Active Fractional PID Controller Based on Whale's Optimization Algorithm for Stabilizing a Quarter Vehicle Suspension System

Karam

Awad

2020

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

Improving the dynamic performance of an automobile suspension system is considered as the main demand for comfortable and safe passenger travelling. From all previously proposed and implemented works, it is noticed that there are other factors that need to be considered to raising the car holding and stability in the road for improved passenger comfort when travelling. The minimization of car body displacement and oscillation time after exposure to road disturbances have been adopted in this work due to their contribution in raising the car holding and stability. The improvement in these features was maintained via a robust control methodology. The Fractional Order PID controller tuned by the Whales Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm is suggested in this work as a robust controller to reduce the effect of these demerits. In this paper, an active quarter car suspension nonlinear system is designed for the presented goals using a robust controller. Minimizing the displacement of the car body and reducing the damping frequency are achieved via a nonlinear control strategy using the fractional order PID controller, which can maintain the required characteristics. Tuning the parameters of the FOPID controller is performed by using the Whales Optimization Algorithm (WOA). Robustness of the FOPID controller is examined and proved to withstand a system parameter variation of ±12 % in all system parameters and a maximum of ±80 % in controller parameter variation. Simulation outcomes also indicate a considerably improved performance of the active suspension system with the fractional order PID controller over the traditional PID.

show abstract

Rapidly-implementable optimizely-sizable fuzzy controller architectures: A performance analysis for semiconductor packaging two axes table

Cited by 4 publications

References 13 publications

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

Artificially-intelligent robotic space manipulator using fuzzily-architected nonlinear controllers

Design, Implementation, Interfacing and Control of Internet of Robot Things for Assisting Robot

Design of Active Fractional PID Controller Based on Whale's Optimization Algorithm for Stabilizing a Quarter Vehicle Suspension System

Contact Info

Product

Resources

About