Embedded fuzzy-control system for machining processes

Haber, Rodolfo E.; Alique, José R.; Alique, A.; Hernández, J.; Uribe-Etxebarria, Ramón

doi:10.1016/s0166-3615(03)00022-8

Cited by 36 publications

(13 citation statements)

References 13 publications

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“…Future work will be focused on the combination of different data-driven techniques with other fuzzy logic techniques [61][62][63][64][65][66][67] targeting experimental validation on different nonlinear laboratory equipment and real-world applications including large-scale systems [61], robotics and autonomous systems [68][69][70][71][72], medicine [73][74][75], engines [76] and combining with fault diagnosis and optimization [77][78][79][80][81][82].…”

Section: Discussionmentioning

confidence: 99%

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

et al. 2019

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In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

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Section: Discussionmentioning

confidence: 99%

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

et al. 2019

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“…In particular, human activities recognition is an important research topic in the new IoT era, in order to analyse the behaviour and improve the human-computer interactions. In the last decades, different computational intelligence techniques have demonstrated their suitability in to optimize industrial processes (Haber et al 2003;Haber-Guerra et al 2006). In particular, artificial neural networks and fuzzy systems have been worldwide applied (Precup et al 2017;Rǎdac et al 2013;Martin and Guerra 2009).…”

Section: Computational Intelligence Applied In Pattern Recognition Anmentioning

confidence: 99%

Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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“…The virtual sensors developed in this work consist of a system for the data acquisition of internal CNC signals, a module for signal processing and an intelligent decision-making scheme. The approaches that can be found in the literature for this task are mainly spectral analysis [ 12 – 14 ], wavelet transforms [ 15 , 16 ], fuzzy logic [ 17 – 19 ], neural networks [ 20 – 22 ], time domain processing [ 14 , 23 , 24 ] and hybrid systems [ 25 , 26 ]. In this paper, the time domain processing approach is considered, where a segmentation of the electrical power consumption takes place before a Bayesian network (BN) analysis is done to identify faults.…”

Section: Improved Diagnosis Of Faults In Multitooth Tool Machining: Amentioning

confidence: 99%

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Bustillo

Correa

Reñones

2011

Sensors

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The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

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Embedded fuzzy-control system for machining processes

Cited by 36 publications

References 13 publications

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

Computational Intelligence for Simulating a LiDAR Sensor

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

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