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GSA-based optimal backstepping controller with a fuzzy compensator for robust control of an autonomous quadrotor UAV

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2015年第5期87卷 493-505页

作者： Basri, Mohd Ariffanan Mohd Husain, Abdul Rashid Danapalasingam, Kumeresan A. Univ Teknol Malaysia Dept Control & Mechatron Engn Skudai Malaysia Univ Teknol Malaysia Dept Control & Mechatron Engn Fac Elect Engn Skudai Malaysia

Purpose - The purpose of this paper is to propose a new approach for robust control of an autonomous quadrotor unmanned aerial vehicle (UAV) in automatic take-off, hovering and landing mission and also to improve the stabilizing performance of the quadrotor with inherent time-varying disturbance. Design/methodology/approach - First, the dynamic model of the aerial vehicle is mathematically formulated. Then, a combination of a nonlinear backstepping scheme with the intelligent fuzzy system as a new key idea to generate a robust controller is designed for the stabilization and altitude tracking of the vehicle. For the problem of determining the backstepping control parameters, a new heuristic algorithm, namely, gravitational search algorithm has been used. Findings - The control law design utilizes the backstepping control methodology that uses Lyapunov function which can guarantee the stability of the nominal model system, whereas the intelligent system is used as a compensator to attenuate the effects caused by external disturbances. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for automatic take-off, hovering and landing mission of quadrotor UAV even in the presence of unknown perturbations. Originality/value - This paper propose a new robust control design approach which incorporates the backstepping control with fuzzy system for quadrotor UAV with inherent time-varying disturbance. The originality of this work relies on the technique to compensate the disturbances acting on the quadrotor UAV. In this new approach, the fuzzy system is introduced as an auxiliary control effort to compensate the effect of disturbances. Because the proposed control technique has the capability of robustness against disturbance, thus, it is also suitable to be applied for a broad class of uncertain nonlinear systems.

关键词： Robust control Backstepping Fuzzy compensator gravitational search algorithm Quadrotor UAV

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Economic analysis to implement distributed generation system in a rail-way rake maintenance depot

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RENEWABLE ENERGY 2015年 78卷 157-164页

作者： Hazra, A. Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

Distributed generation (DG) with renewable energy resources is gaining importance in modern power systems for its environmental beneficiaries. In this work, decentralized generation system is introduced using distributed renewable energy resources considering its economic feasibility a comparative study has made to find the optimal power operation and optimum installable capacities of distributed energy resources (DER) for minimum energy cost. Gravitation search algorithm (GSA) is used to solve this optimization problem. (C) 2014 Published by Elsevier Ltd.

关键词： Distributed generation Economic feasibility Optimal power operation Distributed energy resources gravitational search algorithm

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Optimization of design with estimation of performance parameters of inner rotor permanent magnet synchronous machine used in VAWT using GSA and GSA-PSO

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INTERNATIONAL JOURNAL OF ENERGY AND STATISTICS 2017年第2期5卷

作者： Puri, Vinod Chauhan, Yogesh K. Singh, Nidhi Gautam Buddha Univ Sch Engn Greater Noida India

Permanent Magnet Synchronous Machine has been utilized in numerous applications, especially in WECS. This paper discusses the inner rotor configuration of PMSM i.e. IRPMSM and the design equations have been formulated in terms of design variables or physical parameters. The design formulation of a 500KVA, 3.3KV, 3 phases, 600rpm IRPMSM used in VAWT for WECS has been discussed where;the objective function for design of IRPMSM has been formulated by weight function, while temperature rise, efficiency, regulation and maximum flux density in stator teeth are used as constraints. The parameters estimation of electrical equivalent circuit of PMSM has been done while minimization the error function. With the estimated parameters, the performance of the machine has been estimated. The error function has been formulated using least square method and the error function obtained using output current of the machine. Both the problems have been optimized using GSA and its hybridization with PSO i.e. GSA-PSO. The formulated optimization problems is programmed in MATLAB and results using GSA and GSA-PSO have been obtained with required optimization curved of all the parameters. The sensitivity analyses have been performed using local sensitivity analysis over design parameters of IRPMSM.

关键词： Wind energy conversion system inner rotor permanent magnet synchronous machine vertical axis wind turbine gravitational search algorithm particle swarm optimization sensitivity analysis parameter estimation

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Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks

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IEEE SENSORS JOURNAL 2015年第8期15卷 4264-4274页

作者： RejinaParvin, J. Vasanthanayaki, C. Dr NGP Inst Technol Dept Elect & Commun Engn Coimbatore 641048 Tamil Nadu India Govt Coll Technol Dept Elect & Commun Engn Coimbatore 641013 Tamil Nadu India

Particle swarm optimization (PSO)-based effective clustering in wireless sensor networks is proposed. In the existing optimized energy efficient routing protocol (OEERP), during cluster formation some of the nodes are left out without being a member of any of the cluster which results in residual node formation. Such residual or individual nodes forward the sensed data either directly to the base station or by finding the next best hop by sending many control messages hence reduces the network lifetime. The proposed enhanced-OEERP (E-OEERP) reduces/eliminates such individual node formation and improves the overall network lifetime when compared with the existing protocols. It can be achieved by applying the concepts of PSO and gravitational search algorithm (GSA) for cluster formation and routing, respectively. For each cluster head (CH), a supportive node called cluster assistant node is elected to reduce the overhead of the CH. With the help of PSO, clustering is performed until all the nodes become a member of any of the cluster. This eliminates the individual node formation which results in comparatively better network lifetime. With the concept of GSA, the term force between the CHs is considered for finding the next best hop during route construction phase. The performance of the proposed work in terms of energy consumption, throughput, packet delivery ratio, and network lifetime are evaluated and compared with the existing OEERP, low energy adaptive clustering hierarchy, data routing for in-network aggregation, base-station controlled dynamic clustering protocols. This paper is simulated using NS-2 simulator. The results prove that, the proposed E-OEERP shows better performance in terms of lifetime.

关键词： Cluster formation gravitational search algorithm particle swarm optimization routing

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Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

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CONNECTION SCIENCE 2015年第4期27卷 340-357页

作者： Sheikhan, Mansour Arabi, Mahdi Abbasnezhad Gharavian, Davood Islamic Azad Univ South Tehran Branch Dept Elect Engn POB 11365-4435 Tehran Iran Shahid Beheshti Univ Dept Elect Engn Tehran Iran

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

关键词： emotion recognition speech processing modified Elman neural network optimisation gravitational search algorithm firefly algorithm

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Application of GSA optimized PI controller parameters in automatic generation control for interconnected power system

Application of GSA optimized PI controller parameters in aut...

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International Conference on Signal Processing, Communication, Power and Embedded System

作者： Sangram Keshori mohapatra Bhabani Sankar Dash Pabitra mohan Dash Department of Electrical Engineering C.V.Raman College of Engineering Department of Electrical Engineering Bhubaneswar College of Engineering

ISBN: (纸本)9781509046218

gravitational search algorithm (GSA) based Proportional-Integral (PI) controller for Automatic Generation Control (AGC) of an interconnected power system are analyzed the performance of the proposed two area non-reheat thermal system. GSA optimization employed to search for optimization controller parameters by minimizing the objective function. The objective function uses Integral Time multiply Absolute Error (ITAE) in order to increase the performance of the controller. The performance of the proposed PI controller is compared by tuning the GSA technique and best controller is compared to GA optimized PI controller over wide range of operating condition and change in system parameter

关键词： load frequency control proportional-integral controller gravitational search algorithm automatic generation control

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Automatic Generation Control of Multi-source Interconnected Power System Including DFIG Wind Turbine

Automatic Generation Control of Multi-source Interconnected ...

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International Conference on Power Electronics, Intelligent Control and Energy Systems

作者： Preeti Veena Sharma R. Naresh Harish Pulluri Electrical Engineering Department National Institute of Technology

ISBN: (纸本)9781467385886

This paper presents the solution of automatic generation control (AGC) problem of four-area interconnected power system including DFIG (doubly fed induction generator) wind turbine using gravitational search algorithm (GSA). The GSA is used to tune the gains of the speed as well as pitch angle controller of the DFIG wind turbine along with the proportional integrated derivative (PID) controlled interconnected system. To accelerate the performance of the basic GSA, the opposition based learning concept is embedded in GSA and is known as opposition learning based GSA (OGSA). The simulation results show the effectiveness of the OGSA optimized controllers in comparison to GSA in terms of faster convergence, settling time, overshoot and undershoot of the deviations in frequency and tie-line power. The variation of the wind penetration in the considered power system from 10% to 40% is used to show the competency of the proposed controller. The performance of the proposed controller is also shown for the large load perturbation in the control areas from 0.1 p.u MW to 0.4 p.u MW.

关键词： Frequency Control Wind Turbine Inertia Control Pitch Control gravitational search algorithm Opposition Based Learning

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Optimal coordinate operation control for wind-photovoltaic-battery storage power-generation units

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ENERGY CONVERSION AND MANAGEMENT 2015年 90卷 466-475页

作者： Wu, Kehe Zhou, Huan An, Sicheng Huang, Ting North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Scholl Control & Comp Engn Beijing 102206 Peoples R China Shanghai Municipal Elect Power Co Pudong Power Supply Co Shanghai 200122 Peoples R China

An optimal coordinate operation control method for large-scale wind-photovoltaic (PV)-battery storage power generation units (WPB-PGUs) connected to a power grid with rated power output was proposed to address the challenges of poor stability, lack of decision-making, and low economic benefits. The "rainflow" calculation method was adopted to establish the battery cycle life model and to calculate quantitatively the life expectancy loss in the operation process. To minimize unit cost of power generation, this work optimized the output period of the equipment and strategy of battery charging and discharging with consideration of working conditions, generation equipment characteristics, and load demand by using the enhanced gravitational search algorithm (EGSA). A case study was conducted on the basis of data obtained using WPB-PGU in Zhangbei, China. Results showed that the proposed method could effectively minimize the unit cost of a WPB-PGU under different scenarios and diverse meteorological conditions. The proposed algorithm has high calculation accuracy and fast convergence speed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Coordinate operation Wind-photovoltaic-battery Rated power output Unit cost of power generation gravitational search algorithm

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A chaos embedded GSA-SVM hybrid system for classification

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NEURAL COMPUTING & APPLICATIONS 2015年第3期26卷 713-721页

作者： Li, Chaoshun An, Xueli Li, Ruhai China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100044 Peoples R China Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

Parameter optimization and feature selection influence the classification accuracy of support vector machine (SVM) significantly. In order to improve classification accuracy of SVM, this paper hybridizes chaotic search and gravitational search algorithm (GSA) with SVM and presents a new chaos embedded GSA-SVM (CGSA-SVM) hybrid system. In this system, input feature subsets and the SVM parameters are optimized simultaneously, while GSA is used to optimize the parameters of SVM and chaotic search is embedded in the searching iterations of GSA to optimize the feature subsets. Fourteen UCI datasets are employed to calculate the classification accuracy rate in order to evaluate the developed CGSA-SVM approach. The developed approach is compared with grid search and some other hybrid systems such as GA-SVM, PSO-SVM and GSA-SVM. The results show that the proposed approach achieves high classification accuracy and efficiency compared with well-known similar classifier systems.

关键词： Support vector machine gravitational search algorithm Chaotic search Parameter optimization Feature selection

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A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm

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AI COMMUNICATIONS 2015年第4期28卷 751-764页

作者： Kumar, Yugal Sahoo, G. Birla Inst Technol Dept Comp Sci & Engn Ranchi Jharkhand India

Clustering is a process to discover unseen patterns in a given set of objects. Objects belonging to the same pattern are homogenous in nature while they are heterogeneous in other patterns. In this paper, a hybrid data clustering algorithm comprising of improved cat swarm optimization (CSO) and K-harmonic means (KHM) is proposed to solve the clustering problem. The proposed algorithm exhibits strengths of both the mentioned algorithms, it is named as improved CSOKHM (ICSOKHM). The performance of the proposed algorithm is evaluated using seven datasets and is compared with existing algorithms like KHM, PSO, PSOKHM, ACA, ACAKHM, GSAKHM and CSO. The experimental results demonstrate that the proposed algorithm not only improves the convergence speed of CSO algorithm but also prevents KHM algorithm from running into local optima.

关键词： Cat swarm optimization data clustering K-harmonic means gravitational search algorithm particle swarm optimization

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