V-ANFIS for Dealing with Visual Uncertainty for Force Estimation in Robotic Surgery

Accurate and robust estimation of applied forces in Robotic-Assisted Minimally Invasive Surgery is a very challenging task. Many vision-based solutions attempt to estimate the force by measuring the surface deformation after contacting the surgical tool. However, visual uncertainty, due to tool occlusion, is a major concern and can highly affect the results' precision. In this paper, a novel design of an adaptive neuro-fuzzy inference strategy with a voting step (V-ANFIS) is used to accommodate with this loss of information. Experimental results show a significant accuracy improvement from 50% to 77% with respect to other proposals.Peer ReviewedPostprint (published version

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Improvement of voltage stability for grid connected solar photovoltaic systems using static synchronous compensator with recurrent neural network

Purpose. This article proposes a new control strategy for static synchronous compensator in utility grid system. The proposed photovoltaic fed static synchronous compensator is utilized along with recurrent neural network based reference voltage generation is presented in grid system network. The novelty of the proposed work consists in presenting a Landsman converter enhanced photovoltaic fed static synchronous compensator with recurrent neural network algorithm, to generate voltage and maintain the voltage-gain ratio. Methods. The proposed algorithm which provides sophisticated and cost-effective solution for utilization of adaptive neuro-fuzzy inference system as maximum power point tracking assures controlled output and supports the extraction of complete power from the photovoltaic panel. Grid is interconnected with solar power, voltage phase angle mismatch, harmonic and voltage instability may occur in the distribution grid. The proposed control technique strategy is validated using MATLAB/Simulink software and hardware model to analysis the working performances. Results. The results obtained show that the power quality issue, the proposed system to overcome through elimination of harmonics, reference current generation is necessary, which is accomplished by recurrent neural network. By recurrent neural network, the reference signal is generated more accurately and accordingly the pulses are generated for controlling the inverter. Originality. Compensation of power quality issues, grid stability and harmonic reduction in distribution network by using photovoltaic fed static synchronous compensator is utilized along with recurrent neural network controller. Practical value. The work concerns the comparative study and the application of static synchronous compensator with recurrent neural network controller to achieve a good performance control system of the distribution network system. This article presents a comparative study between the conventional static synchronous compensator, static synchronous compensator with recurrent neural network and hardware implementation with different load. The strategy based on the use of a static synchronous compensator with recurrent neural network algorithm for the control of the continuous voltage stability and harmonic for the distribution network-linear as well as non-linear loads in efficient manner. The study is validated by the simulation results based on MATLAB/Simulink software and hardware model.Мета. У статті пропонується нова стратегія управління статичним синхронним компенсатором в енергосистемі. Запропонований статичний синхронний компенсатор з живленням від фотоелектричних елементів використовується разом з генератором опорної напруги на основі нейронної рекурентної мережі, представленим в мережі енергосистеми. Новизна запропонованої роботи полягає у поданні статичного синхронного компенсатора з покращеним фотоелектричним перетворювачем Ландсмана з алгоритмом рекурентної нейронної мережі для генерації напруги та підтримки коефіцієнта посилення за напругою. Методи. Запропонований алгоритм, який забезпечує ефективне та економічне рішення для використання адаптивної нейро-нечіткої системи логічного виведення як відстеження точки максимальної потужності, забезпечує контрольований вихід та підтримує вилучення повної потужності з фотогальванічної панелі. Мережа взаємопов’язана із сонячною енергією, у розподільній мережі можуть виникати невідповідність фазового кута напруги, гармоніки та нестабільність напруги. Запропонована стратегія методу управління перевіряється з використанням моделей програмного забезпечення MATLAB/Simulink та апаратного забезпечення для аналізу робочих характеристик. Результати. Отримані результати показують, що проблема якості електроенергії, яку запропонована система долає за допомогою усунення гармонік,потребує генерації еталонного струму, що здійснюється рекурентною нейронної мережею. За допомогою рекурентної нейронної мережі більш точно формується еталонний сигнал і відповідно генеруються імпульси для керування інвертором. Оригінальність. Компенсація проблем з якістю електроенергії, стабільністю мережі та зниженням гармонік у розподільній мережі за допомогою статичного синхронного компенсатора з фотоелектричним живленням використовується разом із контролером рекурентної нейронної мережі. Практична цінність. Робота стосується порівняльного дослідження та застосування статичного синхронного компенсатора з рекурентним нейромережевим контролером для досягнення хорошої продуктивності системи управління системою розподільної мережі. У цій статті представлено порівняльне дослідження традиційного статичного синхронного компенсатора, статичного синхронного компенсатора з рекурентною нейронною мережею та апаратною реалізацією з різним навантаженням. Стратегія, що ґрунтується на використанні статичного синхронного компенсатора з рекурентним алгоритмом нейронної мережі для ефективного контролю стабільності постійної напруги та гармонік для лінійних та нелінійних навантажень розподільної мережі. Дослідження підтверджується результатами моделювання з урахуванням програмно-апаратної моделі MATLAB/Simulink

Classification of Occluded Objects using Fast Recurrent Processing

Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities. However, the computational demand for these algorithms is too high to work in real time, without specialized hardware or software solutions. In this paper, we propose a framework for augmenting recurrent processing capabilities into a feedforward network without sacrificing much from computational efficiency. We assume a mixture model and generate samples of the last hidden layer according to the class decisions of the output layer, modify the hidden layer activity using the samples, and propagate to lower layers. For visual occlusion problem, the iterative procedure emulates feedforward-feedback loop, filling-in the missing hidden layer activity with meaningful representations. The proposed algorithm is tested on a widely used dataset, and shown to achieve 2$\times$ improvement in classification accuracy for occluded objects. When compared to Restricted Boltzmann Machines, our algorithm shows superior performance for occluded object classification.Comment: arXiv admin note: text overlap with arXiv:1409.8576 by other author

Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.This research was funded by Basque Government and UPV/EHU projects