Розробка комбінованої системи автоматичного керування рухом підводного апарата на базі регулятора з передбаченням та narma-l2-радником

Блінцов, С. В. Розробка комбінованої системи автоматичного керування рухом підводного апарата на базі регулятора з передбаченням та narma-l2-радником = Development of the combined automatic control of the motion of the underwater vehicle based on prediction narma-l2 controller / С. В. Блінцов // Вісн. НУК. – Миколаїв, 2014. – № 2. – Режим доступу : http://evn.nuos.edu.ua/article/view/44071/40332Розглянуто синтез нейромережевого регулятора на базі алгоритму NARMA-L2, а також удосконалення за його допомогою нейрорегулятора з передбаченням для автоматичного керування швидкістю руху підводного апарата. Досліджено ефективність указаних систем керування

Genetic Algorithm & Fuzzy Logic Based PEM Fuel Cells Power Conversion System for AC Integration

In the scientific environment, the leading variables such as voltage, current, power, heat from cooling system, membrane temperature and hydrogen pressure are uses as steady state and transient behaviors of Fuel Cells (FC). In the reproducing process of Fuel Cells (FC) variations, DC-DC converters are connected transversely its terminals, the efficiency, stability and durability are considered as operational problems for steady state. Since the Proton Exchange Fuel Cell is a non-linear process and its parameters change when it is delivering energy to the grid. The conventional controllers can’t content the control objectives. In this paper, an intelligent DC-AC power optimization is proposed for Fuel Cell (FC) control system to produce energy in the grid stations and to improve the power quality when FC is supplying load to grid. Furthermore, a Genetic Algorithm (GA) based reactive power optimization for voltage profile improvement and real power minimization in DC-AC system. A fuzzy logic controller is also used to control active power of PEM fuel cell system. Fuzzy logic controller will modify the hydrogen flow feedback from the terminal load. At the end, we will simulate DC-AC converter for checking its efficiency, stability and durability on the basis of the genetic algorithm and fuzzy logic controller to control power generation

A new class of wavelet networks for nonlinear system identification

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions

Синтез Neuro-Fuzzy-контролерів для керування роботою ГПА

Питання синтезу систем автоматичного керування газоперекачувальними агрегатами магістральних газопроводів на засадах нечіткого логічного виводу та нейромережевого інструментарію з метою забезпечення найкращої якості регулювання, зокрема з врахуванням впливу на динаміку регулювання прилеглих ділянок газопроводу залишається на даний час актуальною і мало дослідженою проблемою, незважаючи на ідеї та теоретичні напрацювання, закладені в діючі штатні системи агрегатної автоматики. Для одного з основних регульованих параметрів компресорного агрегату синтезовані системи регулювання з використанням класичного регулятора, гібридного контролера з нечітким логічним виводом та нейроконтролера з лінеаризацією відгуку. Проведено аналіз роботи досліджуваних систем керування за результатами імітаційного моделювання для різних типів збурюючих чинників та зроблено висновки про границі і їх застосування на реальних об’єктах.The problem of automatic control systems synthesis of main gas pipelines compressor units based on the principles of indistinct inference and neural network tools with the aim to provide the best quality control remains vital and researched a little at present, especially considering the influence on dynamics control of the adjacent areas on the pipeline, despite the ideas and theoretical developments which are the basis of functioning aggregate automation systems. For one of the main adjustable parameters of compressor unit control systems are synthesized with the use of the classic controller, hybrid controller with indistinct inference and neurocontroller with linearization feedback. The analysis of the researched systems work according to the results of simulation modeling for different types of disturbing factors is conducted and conclusion on the limits of their performance on real objects are drawn

Filtered-X Radial Basis Function Neural Networks for Active Noise Control

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response

Filtered-X Radial Basis Function Neural Networks for Active Noise Control

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response