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Identification-Based Robust Motion Control of an AUV: Optimized by Particle Swarm Optimization algorithm

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2017年第2期85卷 331-352页

作者： Mousavian, Sayed Hamid Koofigar, Hamid Reza Univ Isfahan Dept Elect Engn Esfahan *** Iran

In this paper, the problem of identification-based robust motion control of an Autonomous Underwater Vehicle (AUV) is investigated. The unknown system parameters are estimated by using an adaptive parameter identifier, whose gains are optimized by Particle Swarm Optimization (pso) algorithm. Removing the trial and error procedure and ensuring the convergence property together with fast response, are the benefits of such identification scheme. On the other hand, the system uncertainties, hydrodynamic parameter variations and external disturbances which affect the identified dynamical model, are also taken into account. The cross-coupling effects between subsystems are also considered as model uncertainties. Such uncertain model is then adopted in control synthesis procedure, in the steering and diving modes. In order to achieve the robust stability and performance, two robust control strategies are presented here to solve the motion control problem. First, an mixed sensitivity problem is formulated in which the weighting functions are selected based on an optimization criterion, by using pso algorithm. Controller order reduction is also applied to the resulting diving and steering controllers, using the Hankel norm approximation. Then, an Adaptive Sliding Mode Control (ASMC), whose sliding surface coefficients are optimized by pso algorithm, is developed for the identified AUV model. Possessing the robustness properties with respect to system perturbations, the developed Sliding Mode Control (SMC) removes the complexity of uncertain model representation and the limitations on choosing the weighting functions in the control problem. The upper bounds of perturbations are not required to be known in the proposed control schemes. The simulation results are also presented to demonstrate the performance of the proposed identification-based control methods.

关键词： Autonomous underwater vehicle Robust control Adaptive identification Model uncertainty pso algorithm

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A Method for Load Classification and Energy Scheduling Optimization to Improve Load Reliability

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ENERGIES 2018年第6期11卷 1558-1558页

作者： Ren, Yinze Wu, Hongbin Yang, Hejun Yang, Shihai Li, Zhixin Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China State Grid Jiangsu Elect Power Co Ltd Nanjing 210024 Jiangsu Peoples R China

With the large amount of distributed generation in use, the structure of the distribution system is increasingly complex. Therefore, it is necessary to establish a method to improve load reliability. Based on the reliability model of distributed generation, this paper investigates the time sequential simulation of a wind/solar/storage combined power supply system under off-grid operation. After classifying the load by power supply region, the load weight coefficient is established, which modifies the reliability index of the load point and system. The modified expected energy not supplied (EENS) is adopted as the objective function, and the particle swarm optimization algorithm is used to solving the optimal energy scheduling for improving the load reliability. Finally, the load reliability is calculated with a hybrid method. Using the IEEE-RBTS Bus 6 system as an example, the correctness and validity of the proposed method are verified as an effective way to improve load reliability.

关键词： load classification energy scheduling load reliability pso algorithm

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Effectiveness Evaluation for UAV Air-to-Ground Attack Based on pso-BP Neural Network 36

Effectiveness Evaluation for UAV Air-to-Ground Attack Based ...

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第36届中国控制会议

作者： Xia Chen Naikuan Hu School of Automation Shenyang Aerospace University

ISBN: (纸本)9781538629185

In order to adapt to the increasingly rapid air combat rhythm and provide key basis for optimal operational decision,how to evaluate the operational effectiveness of unmanned aerial vehicle（UAV） quickly and accurately is a basic and important research topic for UAV combat *** the problem of effectiveness evaluation,this paper presents an effectiveness evaluation model for UAV air-to-ground attack which is based on pso-BP neural ***,the main influencing factors of effectiveness evaluation of air-to-ground attack are analyzed and ***,the basic principle of pso algorithm and BP neural network are introduced respectively and then pso-BP neural network is ***,simulation experiments are performed using historical statistics and the simulation results show that the proposed method can reduce the training error and improve the evaluation accuracy,which implies that the proposed algorithm is a better method to solve the problem of effectiveness evaluation of UAV air-to-ground attack.

关键词： Effectiveness evaluation UAV air-to-ground attack pso algorithm BP neural network pso-BP neural network

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Research on Fault Diagnosis for Air Drilling Based on an Improved pso for Optimization of Fuzzy Neural Network 10th

Research on Fault Diagnosis for Air Drilling Based on an Imp...

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10th International Conference on Management Science and Engineering Management (ICMSEM)

作者： Zhang, Dan Zhu, Tingting Lv, Yiqing Hou, Jing He, Yue Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

ISBN: (纸本)9789811018374;9789811018367

The air drilling technology, which is a new kind of drilling technology, has got a wide range of applications at home and abroad. Compared with conventional drilling technology, it has a large number of advantages, such as lower cost, faster drilling speed and less pollution. However, lots of questions like drilling tools out of operation frequently were exposed during the gas drilling which restricted the development of air drilling technology. Thus, it has great significance to hammer at studying the air drilling accident diagnostic techniques. The paper has a comprehensive introduction about air drilling principle and the cause of the accident in the process of air drilling. Meanwhile, the paper has proposed an improved particle swarm algorithm for optimization of fuzzy neural network based on new development in automation and intelligent technology and has carried out a positive analysis by using it. The new model in the convergence speed, adaptive value and diagnostic error, etc. is the optimal by comparing with BP neural network and pso neural network. It can improve the subjective shortcoming of traditional methods that the staff in the field operation analyses data through the real-time monitoring system and the air drilling experts identify some characteristics of air drilling accident causes to reduce errors, improve the accuracy of diagnostics and become more intelligent.

关键词： Air and gas drilling Fault diagnosis pso algorithm Fuzzy neural network

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Acoustic Localization of Partial Discharge Sources in Power Transformers Using a Particle-Swarm-Optimization-Route-Searching algorithm

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2017年第6期24卷 3647-3656页

作者： Wang, Yan-Bo Chang, Ding-Ge Fan, Yu-Hang Zhang, Guan-Jun Zhan, Jiang-Yang Shao, Xian-Jun He, Wen-Lin Xi An Jiao Tong Univ Sch Elect Engn State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China State Grid Zhejiang Elect Power Co Res Inst Hangzhou 310014 Zhejiang Peoples R China

Methods to infer the location of partial discharge (PD) in high-power transformers using acoustic emission (AE) data have been extensively studied. The inner complex structure of the transformers is one of the most critical points in localization with AE method. Windings and cores affect acoustic wave propagation by changing the arrival time because of inhomogeneous propagation. A transformer physical model has been established herein by taking these complex factors into consideration. Each node in the model is a potential PD position, and an acoustic wave route comprise a series of nodes. The velocity and propagation factors are set for each node according to its acoustic wave propagation characteristics. A propagation-time estimation algorithm is proposed to calculate the propagation-time. Based on the transformer physical model, a particles-warm-optimization route-searching (psoRS) algorithm is employed for searching the position of the PD source. By comparing time differences of measured AE signals and the ones estimated by the psoRS algorithm, the velocities and positions of particles are continually adjusted, which can ensure their convergence to the PD source position. Localization experiments were performed in 35 and 110 kV transformers, respectively, to verify the applicability of the proposed algorithm. A protrusion defect is used to trigger PD pulses, and four AE sensors with two different arrangements are employed. The results confirm that the accuracy of proposed localization method is insensitive to the presence of metal structures blocking acoustic wave routes.

关键词： Partial discharge localization acoustic measurements power transformers pso algorithm

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A pso-Based Layout Method for GNSS Pseudolite System 2017

A PSO-Based Layout Method for GNSS Pseudolite System

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International Conference on Information Technology (ICIT)

作者： Wang, Jian Li, Hongxin Lu, Jinzhi Li, Kun Li, Huan Yang, Lian Li, Yubai Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China Shanghai Aerosp Elect Technol Inst Shanghai 201109 Peoples R China KTH Royal Inst & Technol S-10044 Stockholm Sweden

ISBN: (纸本)9781450363518

In order to improve the user positioning accuracy in GNSS (Global Navigation Satellite System) pseudolite system, we propose a pso (Particle Swarm Optimization)-based method to optimize the pseudolite layout in this paper. In detail, given the pseudolite layout information, we calculate the system GDOP (Geometric Dilution of Precision) and then minimize it by using a pso-based algorithm with N particles. Here the first particle indicates the classical layout under the given scenario and the other particles separately represent N-1 randomly generated layouts. In each iteration of our pso-based algorithm, these particles move to a direction to reduce the GDOP value. After several iterations, the GDOP value can be minimized and the optimal pseudolite layout is found out as well. To evaluate the merits of our method, we perform some experiments. The experimental results show that compared to the classical pseudolite layout, our method can reduce the GDOP by 13.4%. This, with no doubt, improves the user positioning accuracy. For example, when the pseudo-range error is about 1%, the user positioning accuracy in our optimized layout can be improved by 12.4% against the classical layout.

关键词： Pseudolite Layout Positioning GDOP pso algorithm

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Dynamic modeling and control of IPMC hydrodynamic propulsor

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SMART STRUCTURES AND SYSTEMS 2017年第4期20卷 499-508页

作者： Agrahari, Shivendra K. Mukherjee, Sujoy PDPM Indian Inst Informat & Technol Design & Mfg Jabalpur 482005 Madhya Pradesh India

The ionic polymer metal composite (IPMC) is an electroactive polymer material and has a promising potential as actuators for propulsion and locomotion in underwater systems. In this paper a physics based model is used to analyse the actuation dynamics of the IPMC propulsor. Moreover, proportional-integral (PI) controller is used for position control of the tip displacement of IPMC propulsor. PI parameter tuning is performed using particle swarm optimization (pso) algorithm. Several performance indices have been used as an objective function to optimize the error of the system. Finally, the best tuning method is found out by comparing the results under various performance indices.

关键词： Ionic Polymer Metal Composite (IPMC) smart materials propulsor PI controller pso algorithm

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Robust Optimal Adaptive Trajectory Tracking Control of Quadrotor Helicopter

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LATIN AMERICAN JOURNAL OF SOLIDS AND STRUCTURES 2017年第6期14卷 1040-1063页

作者： Navabi, M. Mirzaei, H. Shahid Beheshti Univ Fac New Technol Engn Tehran *** Iran

This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV) in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (pso) is developed for multi-input multi-output (MIMO) nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC) technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.

关键词： Quadrotor Adaptive Control Nonlinear Disturbance Observer pso algorithm

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Integration of evolutionary computation algorithms and new AUTO-TLBO technique in the speaker clustering stage for speaker diarization of broadcast news

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2017年第1期2017卷 1页

作者： Dabbabi, Karim Hajji, Salah Cherif, Adnen Univ Tunis El Manar Res Unity Anal & Proc Elect & Energet Syst Fac Sci Tunis Tunis 2092 Tunisia Univ Tunis El Manar Natl Sch Engn Tunis Res Lab Informat & Image Proc Tunis 2092 Tunisia

The task of speaker diarization is to answer the question "who spoke when?" In this paper, we present different clustering approaches which consist of Evolutionary Computation algorithms (ECAs) such as Genetic algorithm (GA), Particle Swarm Optimization (pso) algorithm, and Differential Evolution (DE) algorithm as well as Teaching-Learning-Based Optimization (TLBO) technique as a new optimization technique at the aim to optimize the number of clusters in the speaker clustering stage which remains a challenging problem. Clustering validity indexes, such as Within-Class Distance (WCD) index, Davies and Bouldin (DB) index, and Contemporary Document (CD) index, is also used in order to make a correction for each possible grouping of speakers' segments. The proposed algorithms are evaluated on News Broadcast database (NDTV), and their performance comparisons are made between each another as well as with some well-known clustering algorithms. Results show the superiority of the new AUTO-TLBO technique in terms of comparative results obtained on NDTV, RT-04F, and ESTER datasets of News Broadcast.

关键词： Speaker diarization pso algorithm GA algorithm DE algorithm TLBO technique EA algorithms Clustering validity index DER

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Particle swarm optimization algorithm for ignition of hydrogen isotopes (deuterium-tritium) pellets

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2017年第14期42卷 9099-9106页

作者： Livari, Asqhar Ali Malekynia, B. Livari, Akbar Ali Urmia Univ Fac Sci Dept Phys Orumiyeh Iran Islamic Azad Univ Dept Phys Gachsaran Branch Gachsaran Iran

Recent improvements on high power lasers have made access to dense and hot plasmas possible realizing the inertial confinement fusion (ICF). Results from Osaka University experiments found that higher energy gains are reachable through ideal adiabatic shock free volume ignition. Laser pulses limit the range of laser input energy. Thus, it becomes necessary to make efficient use of available energy to obtain the highest gain. The present paper aims to investigate the attainment of highest gain for D-T fuel using this energy with iteration model. It is shown that the maximum gain of 1.36 x 10(6) is obtained only at input energy of 7.27 x 10(10) J, volume of 0.586 cm(3) and 8133times of solid state density. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： pso algorithm Iteration Energy gain Volume ignition DT fuel Laser energy

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