Fuzzy Adaptive Neurons Applied to the Identification of Parameters and Trajectory Tracking Control of a Multi-Rotor Unmanned Aerial Vehicle Based on Experimental Aerodynamic Data

Ramírez-Mendoza, Abigail María Elena; Covarrubias-Fabela, Roberto; Amézquita-Brooks, Luis; Garcia‐Salazar, Octavio; Yu, Wen

doi:10.1007/s10846-020-01198-w

Cited by 13 publications

(6 citation statements)

References 20 publications

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“…In this study, we prefer to define the nonlinear functions ( and ) with FLSs, as they are powerful tools to represent nonlinearity [34, 35]. Moreover, we observed from the presented experimental results that there is a huge amount of uncertainty, which has even heteroscedastic characteristics.…”

Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Peker,

Beşer,

Işıldar

et al. 2023

Robotica

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We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

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Section: Data-driven Modeling: Capsule–electromagnet Array Interactionmentioning

confidence: 99%

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Peker,

Beşer,

Işıldar

et al. 2023

Robotica

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“…The problem of voice recognition using solfeggio syllables in Spanish is solved by comparing the methods of FAN-activation function step-type (STEPAF)-SPKAF, the Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF. This article is divided into the following sections: Section 2 presents the methods, a description of the FAN model with a Gupta Integrator [7][8][9], and the new SPKAF model for fuzzy neurons from Ramírez-Mendoza et al [5,10,11,29,[32][33][34][35]. Section 3 describes the models and algorithms of fuzzy systems based on ANFIS and FAN-SPKAF for the processing of spike-time-encoded information and pattern recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Spike patterns recognition of the SI syllable in a melody is performed by obtaining the maximum of the STFT magnitudes and the Augmented FAN-STEPAF-SPKAF method [29][30][31][32][33][34][35].…”

Section: Augmented Fan-stepaf-spkaf Model For Syllable-based Speech R...mentioning

confidence: 99%

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

Ramírez-Mendoza

2023

Mathematics

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This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step activation function (STEPAF)-SPKAF, Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF, achieving very good results.

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“…Multirotor UAVs are capable of traversing 3D space and are often chosen for exploration and remote sensing in cluttered environments [4], especially when ground robots might fail. Their interesting dynamics makes them still a common choice for demonstrating novel techniques in control theory [5]. In addition, their flight properties, most of all the ability to hover, make them excellent for carrying sensors and for aiding research in distributed and remote sensing.…”

Section: Introductionmentioning

confidence: 99%

“…State-of-the-art research in computer vision and in SLAM algorithms is often accompanied by functioning ROS implementations of the published methods [13,14,15]. Finally, open-source robotic simulators such Gazebo 5 and CoppeliaSim 6 (previously V-REP) provide integration with ROS. These features allow researchers to focus their research more narrowly rather than on implementation aspects of a whole robotics pipeline.…”

Section: Introductionmentioning

confidence: 99%

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

Baca,

Petrlik,

Vrba

et al. 2020

Preprint

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We present a multirotor Unmanned Aerial Vehicle (UAV) control and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation (http://github.com/ctu-mrs/mrs_uav_system), including realistic simulation of UAVs, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that subsequently shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the Czech Technical University in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA Subterranean challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the competitions and challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.

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Fuzzy Adaptive Neurons Applied to the Identification of Parameters and Trajectory Tracking Control of a Multi-Rotor Unmanned Aerial Vehicle Based on Experimental Aerodynamic Data

Cited by 13 publications

References 20 publications

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

Towards capsule endoscope locomotion in large volumes: design, fuzzy modeling, and testing

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

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