scholarly journals Application of neural models as controllers in mobile robot velocity control loop

2017 ◽  
Vol 68 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Jakub Cerkala ◽  
Anna Jadlovska
Keyword(s):  
Mobile Robot ◽  
Simulation Experiment ◽  
Inverse Dynamics ◽  
Dynamic Properties ◽  
Neural Model ◽  
Input Voltage ◽  
Velocity Control ◽  
Neural Models ◽  
Control Task ◽  
Pi Controllers

Abstract This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

scholarly journals Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 307
Author(s):  
Dawid Wojcieszak ◽  
Maciej Zaborowicz ◽  
Jacek Przybył ◽  
Piotr Boniecki ◽  
Aleksander Jędruś
Keyword(s):  
Image Analysis ◽  
Organic Matter ◽  
Dry Matter ◽  
Neural Model ◽  
Original Method ◽  
Neural Models ◽  
Test Error ◽  
Effective Assessment ◽  
Modelling Methods

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

scholarly journals DYNAMICS BASED CONTROL OF A SKID STEERING MOBILE ROBOT

2016 ◽  
Vol 9 (2) ◽  
pp. 70 ◽  
Author(s):  
Osama Elshazly ◽  
Hossam Abbas ◽  
Zakarya Zyada
Keyword(s):  
Mobile Robot ◽  
Inverse Dynamics ◽  
Linear Quadratic Regulator ◽  
Nonlinear Controller ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Lqr Control ◽  
Control Schemes ◽  
Skid Steering ◽  
Drive Model

In this paper, development of a reduced order, augmented dynamics-drive model that combines both the dynamics and drive subsystems of the skid steering mobile robot (SSMR) is presented. A Linear Quadratic Regulator (LQR) control algorithm with feed-forward compensation of the disturbances part included in the reduced order augmented dynamics-drive model is designed. The proposed controller has many advantages such as its simplicity in terms of design and implementation in comparison with complex nonlinear control schemes that are usually designed for this system. Moreover, the good performance is also provided by the controller for the SSMR comparable with a nonlinear controller based on the inverse dynamics which depends on the availability of an accurate model describing the system. Simulation results illustrate the effectiveness and enhancement provided by the proposed controller.

scholarly journals Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network

2009 ◽  
Vol 2 (1) ◽  
pp. 21-27 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Genetic Model ◽  
Neural Model ◽  
Model Verification ◽  
Design Criteria ◽  
Experimental Setup ◽  
Mean Square ◽  
Raw Water ◽  
Neural Models ◽  
Sand Filter ◽  
Slow Sand Filter

Abstract. The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the present study is to validate HRF developed in the laboratory with Slow Sand Filter (SSF) as a pretreatment unit with the help of Weglin's design criteria for HRF with respect to raw water condition and neuro-genetic model developed based on the filter dataset. The results achieved from the three different models were compared to find whether the performance of the experimental HRF with SSF output conforms to the other two models which will verify the validity of the former. According to the results, the experimental setup was coherent with the neural model but incoherent with the results from Weglin's formula as lowest mean square error was observed in case of the neuro-genetic model while comparing with the values found from the experimental SSF-HRF unit. As neural models are known to learn a problem with utmost efficiency, the model verification result was taken as positive.

scholarly journals A Goal-Driven Tree-Structured Neural Model for Math Word Problems

2019 ◽  
Author(s):  
Zhipeng Xie ◽  
Shichao Sun
Keyword(s):  
Word Problems ◽  
State Of The Art ◽  
Neural Model ◽  
Neural Models ◽  
Top Down ◽  
Feedforward Networks ◽  
Whole Process ◽  
Expression Tree ◽  
Generate Solution ◽  
Human Problem

Most existing neural models for math word problems exploit Seq2Seq model to generate solution expressions sequentially from left to right, whose results are far from satisfactory due to the lack of goal-driven mechanism commonly seen in human problem solving. This paper proposes a tree-structured neural model to generate expression tree in a goal-driven manner. Given a math word problem, the model first identifies and encodes its goal to achieve, and then the goal gets decomposed into sub-goals combined by an operator in a top-down recursive way. The whole process is repeated until the goal is simple enough to be realized by a known quantity as leaf node. During the process, two-layer gated-feedforward networks are designed to implement each step of goal decomposition, and a recursive neural network is used to encode fulfilled subtrees into subtree embeddings, which provides a better representation of subtrees than the simple goals of subtrees. Experimental results on the dataset Math23K have shown that our tree-structured model outperforms significantly several state-of-the-art models.

A Neural Model for the Visual Navigation and Planning of a Mobile Robot

1999 ◽  
pp. 319-323 ◽  
Author(s):  
Mathias Quoy ◽  
Philippe Gaussier ◽  
Sacha Leprêtre ◽  
Arnaud Revel
Keyword(s):  
Mobile Robot ◽  
Neural Model ◽  
Visual Navigation

MREM, Discrete Recurrent Network for Optimization

2011 ◽  
pp. 1112-1120
Author(s):  
Enrique Mérida-Casermeiro ◽  
Domingo López-Rodríguez ◽  
Juan M. Ortiz-de-Lazcano-Lobato
Keyword(s):  
Neural Networks ◽  
Combinatorial Optimization ◽  
Image Recognition ◽  
Single Neuron ◽  
Optimization Problems ◽  
Neural Model ◽  
Recurrent Network ◽  
Neural Models ◽  
Seminal Work ◽  
Combinatorial Optimization Problems

Since McCulloch and Pitts’ seminal work (McCulloch & Pitts, 1943), several models of discrete neural networks have been proposed, many of them presenting the ability of assigning a discrete value (other than unipolar or bipolar) to the output of a single neuron. These models have focused on a wide variety of applications. One of the most important models was developed by J. Hopfield in (Hopfield, 1982), which has been successfully applied in fields such as pattern and image recognition and reconstruction (Sun et al., 1995), design of analogdigital circuits (Tank & Hopfield, 1986), and, above all, in combinatorial optimization (Hopfield & Tank, 1985) (Takefuji, 1992) (Takefuji & Wang, 1996), among others. The purpose of this work is to review some applications of multivalued neural models to combinatorial optimization problems, focusing specifically on the neural model MREM, since it includes many of the multivalued models in the specialized literature.

scholarly journals Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm

2019 ◽  
Vol 16 (18) ◽  
pp. 3294 ◽  
Author(s):  
Piotr Boniecki ◽  
Małgorzata Idzior-Haufa ◽  
Agnieszka Pilarska ◽  
Krzysztof Pilarski ◽  
Alicja Kolasa-Wiecek
Keyword(s):  
Vector Quantization ◽  
Digital Images ◽  
Neural Model ◽  
Learning Vector Quantization ◽  
Neural Models ◽  
Topological Map ◽  
Compost Maturity ◽  
Feature Map ◽  
Graphic Information

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.

Trajectory estimation of a skid-steering mobile robot propelled by independently driven wheels

Robotica ◽  
2011 ◽  
Vol 30 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Tokuji Okada ◽  
Abeer Mahmoud ◽  
Wagner Tanaka Botelho ◽  
Toshimi Shimizu
Keyword(s):  
Mobile Robot ◽  
Virtual Work ◽  
Mathematical Expression ◽  
Velocity Control ◽  
Trajectory Estimation ◽  
Control Rules ◽  
Ground Conditions ◽  
The Coefficient Of Friction ◽  
Skid Steering ◽  
The Mathematical Model

SUMMARYThis paper analyses a mobile robot with independently rotating wheels travelling on uneven but smooth ground, including ascending or descending surfaces. We formulate a mathematical expression for the energy cost of the robot's movement. For our analysis, we utilise the principle of virtual work and assume that the robot moves with a fixed arrangement of wheel axes and without using a steering handle. The mathematical model reveals that the coefficient of friction and the payload distribution dominate the wheel behaviour, including slipping and skidding. We minimise the virtual work expression to determine the robot's motion complying with driven wheels. The model also enables us to estimate trajectories for different ground conditions. A hybrid robot, PEOPLER-II, is used to demonstrate the predicted motions, including turns and spins, by following angular velocity control rules. Experimental data verifies that the proposed formulation and minimisation of virtual work are valid techniques for predicting a robot's trajectory. The method described is widely applicable to wheeled robots having independently driven wheels.

scholarly journals An ensemble of neural models for nested adverse drug events and medication extraction with subwords

2019 ◽  
Vol 27 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Meizhi Ju ◽  
Nhung T H Nguyen ◽  
Makoto Miwa ◽  
Sophia Ananiadou
Keyword(s):  
Random Field ◽  
Adverse Drug Events ◽  
Conditional Random Field ◽  
Neural Model ◽  
Neural Models ◽  
Data Set ◽  
Conditional Random Field Model ◽  
Semantic Types ◽  
Unknown Words ◽  
Mimic Iii

Abstract Objective This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and Methods We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries. To better represent rare and unknown words in entities, we further tokenized the MIMIC III data set by splitting the words into finer-grained subwords. We finally combined all the models to boost the performance. Additionally, we implemented a featured-based conditional random field model and created an ensemble to combine its predictions with those of the neural model. Results Our method achieved 92.78% lenient micro F1-score, with 95.99% lenient precision, and 89.79% lenient recall, respectively. Experimental results showed that combining the predictions of either multiple models, or of a single model with different settings can improve performance. Discussion Analysis of the development set showed that our neural models can detect more informative text regions than feature-based conditional random field models. Furthermore, most entity types significantly benefit from subword representation, which also allows us to extract sparse entities, especially nested entities. Conclusion The overall results have demonstrated that the ensemble method can accurately recognize entities, including nested and polysemous entities. Additionally, our method can recognize sparse entities by reconsidering the clinical narratives at a finer-grained subword level, rather than at the word level.

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