Fuzzy control of robotic arm implemented in PLC

Skulavik, Tomas; Kopcek, Michal; Alena, Kopeekova

doi:10.1109/icccyb.2013.6617628

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Based on the sample set that is collected through the simulation of the predesigned fuzzy PID control system under various conditions of hoist brakes, the parameters above are obtained through training. 9–11 Figures 4 and 5 show the trained membership function of deceleration error and the change rate of deceleration error, as well as the change amount of proportional coefficient Δ K p , integral coefficient Δ K i , and differential coefficient Δ K d .…”

Section: Training Of the Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

Tian

Xiao

et al. 2014

Advances in Mechanical Engineering

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In comparison with constant torque brakes, constant deceleration brakes are more advantageous for the safety of mining hoists, but complete set of such products manufactured by big companies are not what ordinary mining enterprises can afford. As an alternative solution, this article develops a constant deceleration compensation device, which adds the function of constant deceleration brake onto the original brakes. Control strategy based on Fuzzy Neural Network PID is investigated and simulated with the combination of AMEsim and Simulink. An actual device is built and tested in real industrial field. The application illustrates the feasibility of this constant deceleration compensation device, which can achieve constant decelerations within a very short time. This device will prevent dangerous decelerations from happening to hoists at a much lower cost, and greatly improve the safety and reliability of mining hoists.

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Section: Training Of the Fuzzy Neural Networkmentioning

confidence: 99%

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

Tian

Xiao

et al. 2014

Advances in Mechanical Engineering

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“…As a result, the transmission spectrum of the material is modified based on the ambient lighting [1]. More recently, realizing metamaterial structures has been conducted by simple assembly methodologies which made use of stacking and arranging layers [6][7][8] or by applying simple subtractive operations such as drilling into a block of dielectric material [9]. These processes were time-consuming and suffered from inconsistency [5] but yet served to demonstrate the principle and accelerated interest in an emergent topic.…”

Section: Introductionmentioning

confidence: 99%

Additive manufacturing of metamaterials: A review

Askari

Hutchins

Thomas

et al. 2020

Additive Manufacturing

202

101

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structures which exhibit extraordinary behaviour(s). This review represents a comprehensive account of the state-of-the-art in the production of such metamaterials using additive manufacturing methods and highlights areas, which, based on trends observed in the literature, are worthy of further research and require a coordinated effort on behalf of the afore mentioned disciplines in order to advance the state-of-the-art.

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“…Some researchers have developed low-cost robotic arm controller using a simple controller such as proportional integral (PI) controller [6], PID [7]- [9], fuzzy logic [10], [11], hybrid PID, and fuzzy [12], [13], optimal controller [14], neural network [15] and model-based controller [16]. A few researchers also tried to use robust control to control robotic arm joints such as sliding mode controller [17]- [20] and hybrid PIDsliding mode controller [21].…”

Section: Introductionmentioning

confidence: 99%

Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller

Sutyasadi

Wicaksono

2021

Bulletin EEI

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This paper proposes an improved control strategy of a robotic arm joint using hybrid controller consist of H∞ robust controller and iterative learning controller. The main advantage of this controller is the simple structure that made it possible to be implemented on a small embedded system for frugal innovation in industrial robotic arm development. Although it has a simple structure, it is a robust H∞ controller that has robust stability and robust performance. The iterative learning controller makes the trajectory tracking even better. To test the effectiveness of the proposed method, computer simulations using Matlab and hardware experiments were conducted. Variation of load was applied to both of the processes to present the uncertainties. The superiority of the proposed controller over the proportional integral derivative (PID) controller that usually being used in a low-cost robotic arm development is confirmed that it has better trajectory tracking. The error tracking along the slope of sinusoidal trajectory input was suppressed to zero. The biggest error along the trajectory that happened on every peak of the sinusoidal input, or when the direction is changed has been improved from 15 degrees to 4 degrees. This can be conceived that the proposed controller can be applied to control a low-cost robotic arm joint position which is applicable for small industries or educational purpose.

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Fuzzy control of robotic arm implemented in PLC

Cited by 6 publications

References 2 publications

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

Additive manufacturing of metamaterials: A review

Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller

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