Self-learning interval type-2 fuzzy neural network controllers for trajectory control of a Delta parallel robot

Lu, Xingguo; Zhao, Yue; Liu, Ming

doi:10.1016/j.neucom.2017.12.043

Cited by 34 publications

(12 citation statements)

References 42 publications

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“…In [5], a gain scheduling PID controller optimized by a genetic algorithm is designed for an unmanned marine surface vessel. Lu et al [6] combined an interval type 2 fuzzy neural network with a proportional-derivative controller and applied the resulting scheme to a Delta parallel robot. By means of the Lyapunov stability theory, Mai and Commuri [7] proposed a robust neural network controller for a prosthetic ankle joint with gait recognition.…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

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Section: Introductionmentioning

confidence: 99%

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Bessa

Brinkmann

Duecker

et al. 2018

Mathematical Problems in Engineering

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“…The gradient descent ILC algorithm [25] is used to develop the learning law. The general form of this algorithm is expressed as follow: (27) where k is the repetition number; u is the input applied to the learning process; β is the learning gain; G is transfer function of nominal model; e is the output error; The product of G T e determines the direction of the update vector. The objective of impedance control block is force tracking, so the output error is calculated as:…”

Section: Iterative Learning Controlmentioning

confidence: 99%

Force/Position Hybrid Control for a Hexa Robot Using Gradient Descent Iterative Learning Control Algorithm

Huynh

Kuo

2019

IEEE Access

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This paper presents an approach of the force/position hybrid control for a hexa parallel robot to guarantee a safe and accurate interaction when touching the object surface. A double-loop PID controller is proposed to replace the common PID controller in the position control to eliminate position errors due to the dynamics coupling effect between the arms and the vibration of the mechanical system. An impedance control model is used to guarantee a safe and accurate interaction when touching the object surface. In addition, a gradient descent iterative learning control algorithm is used and modified to determine the optimal impedance parameters in unknown environments. A model of the robot is built in SimMechanics to simulate and estimate system parameters. After that, the experimental work was conducted on a real robot to verify the effectiveness and feasibility of the proposed method. INDEX TERMS Hybrid control, Hexa robot, impedance control, double-loop PID, iterative learning.

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“…These models serve to unite the best of the two methods that act strongly in solving complex problems [53]. Fuzzy neural networks work in issues in the area of the industry [11,54,55], controls, and actuation in robots [56][57][58][59], sectors of the economy [60][61][62], pulsar detection [63] and in the prediction of process failures [64][65][66][67]. Already in health, models have highlights in different performances.…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

Guimarães

Souza

Araújo

et al. 2019

BDCC

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Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

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Self-learning interval type-2 fuzzy neural network controllers for trajectory control of a Delta parallel robot

Cited by 34 publications

References 42 publications

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

Force/Position Hybrid Control for a Hexa Robot Using Gradient Descent Iterative Learning Control Algorithm

Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

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