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International conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (IcAMIMIA)

作者： Santoso, Fendy Garratt, Matthew A. Anavatti, Sreenatha G. Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

ISBN: (纸本)9781538627297

In this research, we develop a self-learning fuzzy logic autopilot for high-performance trajectory tracking of a hexacopter unmanned aerial vehicle. We employ non-linear mathematical models of the system, derived from first principles, to gain more accurate understanding of its dynamic behaviours. Accordingly, we design the TS-fuzzy autopilot of a hexacopter for its altitude loop using the c-means fuzzy clustering technique to control the height of the drone. This research serves as our preliminary study to investigate the feasibility of our fuzzy control system before we can implement it into practice. We perform a systematic comparative study to highlight the effectiveness of our control algorithm. We demonstrate the performance and robustness of the proposed control system in terms of its tracking error with respect to the performance of the conventional PID controller.

关键词： Hexacopter Unmanned Aerial Vehicle Trajectory Tracking fuzzy c-means clustering technique

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clustering technique Applied to Nodal Reliability Indices for Optimal Planning of Energy Resources

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IEEE TRANSAcTIONS ON POWER SYSTEMS 2016年第6期31卷 4679-4690页

作者： Lami, Badr Bhattacharya, Kankar Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

Electric power systems are facing major challenges because of the increase in penetration of energy resources (ERs). This paper focuses on composite system reliability based planning for ERs, and presents novel clustering techniques based approaches to determine the optimal location, size and year of installation of ERs in the system. The K-means clustering and fuzzy c-means clustering techniques are applied to the set of reliability indices, Load Not Served per Interruption (LNSI), which are determined using nodal minimal cut sets. The nodal minimal cut sets are obtained using an optimal power flow (OPF) based approach in this paper. Once the optimal sizes and locations of ERs are obtained, the earliest year of penetration is determined using an adequacy check algorithm. Detailed studies presented considering the IEEE RTS demonstrate the applicability of the proposed technique.

关键词： Energy resources fuzzy c-means clustering technique K-means clustering technique nodal minimal cut sets planning reliability

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Adequacy studies of power systems with barrage-type tidal power plants

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IET RENEWABLE POWER GENERATION 2019年第14期13卷 2612-2622页

作者： Mirzadeh, Mostafa Simab, Mohsen Ghaedi, Amir Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran Islamic Azad Univ Dept Elect Engn Dariun Branch Dariun Iran

Tidal as renewable energy is increasingly utilised for power generation in many countries. Integration of tidal plants into the power system requires considering the intermittent nature of the generated power caused by variable tide levels. Thus, the reliability studies of power systems including high tidal generation affected by the uncertain nature resulting from the variable water levels are investigated. The reliability model of a tidal power plant on a barrage is proposed. In the proposed model, the failure rates of the composed components (especially the hydro pump and gate) and the effect of tidal height variation on the components failure rate are considered. Also, the reliability model with numerous states resulting from the variability of the generated power through the fuzzy c-means clustering technique and Xie-Beni index is reduced to a multi-state reliability model. The resulting reliability model is utilised for the generation adequacy studies of a power system containing large-scale tidal power plants. Moreover, different reliability indices are calculated for future generation expansion planning. The proposed technique is applied to the two-test systems, and sensitivity analysis is performed. Besides, the effects of the hydro pump, maintenance, ageing of the components and peak load are investigated.

关键词： power generation planning fuzzy set theory tidal power stations power generation reliability power generation economics pattern clustering maintenance engineering tidal height variation multistate reliability model resulting reliability model power system large-scale tidal power plants barrage-type tidal power plants power generation variable tide levels high tidal generation variable water levels components failure rate fuzzy c-means clustering technique Xie-Beni index future generation expansion planning

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Extraction, Identification and Regeneration of Source Audio Signal using HHT and SVM 7

Extraction, Identification and Regeneration of Source Audio ...

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7th International conference on computational Intelligence and communication Networks (cIcN)

作者： Sen, Sonali St Xaviers Coll Kolkata India

ISBN: (纸本)9781509000760

This paper presents, the working idea of Separation of each Individual components that make up a Mixed Audio Signals, Identification of each of the Extracted components and finally Wave Regeneration of the components that was separated. That is if the Mixed audio Signal contains components such as "Guitar" and "Violin". Then each of the components of Guitar and Violin will be separated and the individual components will be identified. The Development of this particular working was done using Matlab Simulation and the results were quite fruitful. The Extraction of each of the components can be carried out by Empirical mode Decomposition (it is a part of Hilbert Hyuang Transform) and the Separation to its constituent clusters or classes can be done by fuzzy c-means clustering technique. The Separated components were identified by the SVM classification technique. The SVM classifier's Training and Testing Sets were modified in such way, so that it can predict sound data. The Prediction output of the SVM classification is the class to which the Extracted component belongs (i.e. whether the extracted components are guitar or Flute or Violin etc.). The Extracted components are smoothed using B-spline curve Fitting for the Wave regeneration

关键词： Empirical Mode Decomposition fuzzy c-means clustering technique Support Vector Machine B-spline curve Fitting Iterative Sifting Method control Points IMF components Training Set Testing Set

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Direct Adaptive control Based on LS-SVM Inverse Model For Nonlinear Systems 19

Direct Adaptive Control Based on LS-SVM Inverse Model For No...

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19th International conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mahmoud, Tarek A. Elshenawy, Lamiaa M. Menoufia Univ Fac Elect Engn Ind Elect & Control Engn Dept Menoufia Egypt

ISBN: (纸本)9781479950812

This article proposes an inverse model control scheme for unknown nonlinear systems. The proposed scheme structures the inverse dynamics of the controlled system offline. This is done using a least squares support vector machine (LS-SVM) network that based on the fuzzy c-means (FcM) clustering technique. Then the proposed network is updated online to determine the appropriate control action. A linear feedback compensator is added to ensure the closed loop stability. Simulation results of a continuous stirred tank reactor (cSTR) demonstrate the efficiency of the proposed approach.

关键词： adaptive control fuzzy set theory least squares approximations nonlinear control systems support vector machines cSTR FcM clustering technique LS-SVM inverse model continuous stirred tank reactor control action direct adaptive control fuzzy c-means clustering technique inverse model control scheme least square support vector machines nonlinear systems Adaptation models chemical reactors computational modeling Mathematical model Process control Support vector machines Trajectory adaptive control Support Vector Network continuously stirred tank reactors Nonlinear systems control action least squares approximations Nonlinear control systems chemical reactors fuzzy Set Theory computational modeling Process control trajectory Mathematical Model Adaptation models Inverse method

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