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Vehicle collision avoidance motion planning strategy using artificial potential field with adaptive multi-speed scheduler

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第10期14卷 1200-1209页

作者： Wahid, Nurbaiti Zamzuri, Hairi Amer, Noor H. Dwijotomo, Abdurahman Saruchi, Sarah Atifah Mazlan, Saiful Amri Univ Teknol MARA Fac Elect Engn Terengganu 23000 Malaysia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia eMoovit Technol Sdn Bhd Futurise Ctr Prime Lab 3Level 1Cyber 8 Cyberjaya 63000 Selangor Malaysia Univ Pertahanan Nas Malaysia Dept Mech Engn Kuala Lumpur 57000 Malaysia

This study presents an adaptive motion planning strategy for automated vehicle collision avoidance systems to be associated with the variation of collision speed region based on the position of the obstacle. This is done by designing the motion planner using an artificial potential field (APF) with the incorporation of an adaptive multi-speed scheduler using fuzzy system in the motion planning structure. The knowledge database information is developed based on the risk perception of the driver that consists of APF parameters and was optimised by using particle swarm optimisation algorithm. This study contributes to the improvement of a feasible reference motion generated by the motion planner that can be converted into desired control signals. The reference motion resulted to provide the control command that managed to avoid collision successfully by evasive manoeuvre without lane departure when adapting to variation in the vehicle speeds with different obstacle positions. The results indicated the reduction of the lateral error with respect to the reference avoidance trajectory data of up to 87% compared to base-type APF with maximum reference lateral motion is reduced of up to 26%. Then, a hardware-in-loop test is conducted to verify the proposed strategy using a steering wheel system.

关键词： mobile robots particle swarm optimisation motion control position control collision avoidance adaptive multispeed scheduler adaptive motion planning strategy automated vehicle collision avoidance systems collision speed region motion planner artificial potential field fuzzy system motion planning structure knowledge database information APF parameters particle swarm optimisation algorithm feasible reference motion obstacle positions reference avoidance trajectory data base-type APF maximum reference lateral motion vehicle collision avoidance motion planning strategy

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Adaptive backstepping controller design on Buck converter with a novel improved identification method

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IET CONTROL THEORY AND APPLICATIONS 2022年第5期16卷 485-495页

作者： Saadat, Seyyed Amirhossein Ghamari, Seyyed Morteza Mollaee, Hasan Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Semnan Iran

An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

关键词： system identification particle swarm optimisation algorithm high variance noises identification method least-squares identification algorithm robust dynamics parametric uncertainties PWM invertors robust control three-term control control structure adaptive backstepping controller design load variations power generation control particle swarm optimisation adaptive control PID controller improved identification technique adaptive mechanism DC-DC buck converter adaptive backstepping method Lyapunov methods control system synthesis DC-DC power convertors black-box block pulse width modulation

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Exergetic optimisation of a multi-stage compression transcritical refrigeration cycle

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INTERNATIONAL JOURNAL OF EXERGY 2016年第1期20卷 22-47页

作者： Rashidi, M. M. Parsa, A. Basiri Shamekhi, L. Nazari, F. Ali, M. Tongji Univ Shanghai Key Lab Vehicle Aerodynam & Vehicle Ther 4800 Cao An Rd Shanghai 201804 Peoples R China ENN Tongji Clean Energy Inst Adv Studies Shanghai Peoples R China Islamic Azad Univ Young Researchers & Elite Club Hamedan Branch Hamadan *** Iran Univ Paris 10 LTIE GTE EA 4415 50 Rue Sevres F-92410 Ville Davray France King Saud Univ Dept Mech Engn Coll Engn POB 800 Riyadh 11421 Saudi Arabia

Thermodynamic analyses as well as optimisation studies based on maximum cooling coefficient of performance (COP) as thermal efficiency and optimum values of exergy efficiency and total input work of a three-stage compression transcritical CO2 cycle have been presented. A new triple-stage configuration of compression is presented, which uses two intercoolers to enhance the performance. For parametric optimisation, COP and total input work are selected as objective functions, which are calculated for different values of outlet pressure of first and second compressors. For optimisation, a procedure based on artificial neural network and particle swarm optimisation (PSO) algorithm is proposed. The procedure includes two stages. According to the parametric analysis data, in the first stage two different multilayer perceptron neural networks are trained. In the next stage, two distinct PSO algorithms are used to optimise total input work and thermal efficiency.

关键词： CO2 refrigerant COP coefficient of performance exergy efficiency Artificial neural network particle swarm optimisation algorithm

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Cost-based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第8期12卷 1712-1720页

作者： Simorgh, Hamid Doagou-Mojarrad, Hasan Razmi, Hadi Gharehpetian, Gevork B. Microelect Res & Dev Ctr Iran MERDCI Tehran Iran Islamic Azad Univ East Tehran Branch Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran

Here, the optimal placement and sizing of electric vehicle charging stations (EVCSs) are presented. High penetration of electric vehicles (EVs) and resulted losses in network would consequently impose more complexity to solution of application problem of EVCSs. To overcome this problem, the model would consider the incentive-based demand response programmes (DRPs), which is handled by particle swarm optimisation algorithm. Minimising investment cost, connection cost, total cost of losses, and demand response (DR) cost are the objective functions of this problem here. Finally, the proposed model is applied to a test system and results are discussed. By comparing the results obtained through different scenarios, it is concluded that the application of DRP results in a distinct reduction in grid losses and total costs.

关键词： electric vehicle charging secondary cells particle swarm optimisation cost-based optimal siting optimal sizing electric vehicle charging stations EVC incentive-based demand response programmes particle swarm optimisation algorithm test system DRP

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Test cases generation for multiple paths based on PSO algorithm with metamorphic relations

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IET SOFTWARE 2018年第4期12卷 306-317页

作者： Lv, Xue-Wei Huang, Song Hui, Zhan-Wei Ji, Hai-Jin Army Engn Univ PLA Command Informat Inst Software Engn Dept Nanjing Jiangsu Peoples R China Huaiyin Normal Univ Sch Comp Sci & Technol Huaian Peoples R China PLA Mil Training Software Testing & Evaluat Ctr Test Management Dept Nanjing Jiangsu Peoples R China

The generation of multiple-path test cases can greatly enhance the efficiency of path-wise testing. Various methods adopting meta-heuristic algorithm to generate multiple-path test cases have been proposed, but existing methods focus on improving the meta-heuristic algorithm to get better test case generation efficiency, and test cases covering each path needs to be generated by meta-heuristic algorithm searching. To improve efficiency, a test case generation method for multiple-path coverage is proposed in this study, which combines a particle swarm optimisation (PSO) algorithm with metamorphic relations (MRs). The method first generates a test case using the PSO algorithm, and then generates new test cases by repeatedly using MRs between test cases. This method reduces evolving numbers of PSO algorithm. The experimental results show that the proposed method can significantly enhance the efficiency in terms of fitness evaluations and time consumption.

关键词： particle swarm optimisation program testing search problems metamorphic relations PSO algorithm test cases generation multiple-path test cases path-wise testing test case generation efficiency test case generation method multiple-path coverage particle swarm optimisation algorithm metaheuristic algorithm searching

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Side lobe level reduction and gain enhancement of a pyramidal horn antenna in the presence of metasurfaces

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IET MICROWAVES ANTENNAS & PROPAGATION 2018年第3期12卷 295-301页

作者： Moradi, Anis Mohajeri, Farzad Shiraz Univ Dept Commun & Elect Zand Blvd Shiraz Iran

In this study, the design method and experimental presentation of low-refractive-index metasurfaces have been reported. These metasurfaces are employed in the interior E-plane walls of a pyramidal horn antenna. The target is a linearly polarised horn antenna with low side lobe levels (SLLs) and symmetric radiation patterns in the K-u band (13-18GHz) for satellite communications. particle swarm optimisation algorithm has been applied to the design procedure to achieve low-loss dispersion-engineered metasurfaces with desirable surface impedance characteristics. The metasurfaces were fabricated using precise low-cost printed-circuit board technique. There was perfect agreement between the measurement and simulation results. The fabricated horn showed low SLLs and an increase in the gain across the entire bandwidth. The SLLs are reduced by 14-29dB in the E-planes and the gain is improved by 1.2-2.3dB compared with a corresponding conventional horn. The far field radiation patterns of the metahorn verify the metasurface design approach. Furthermore, this method assures a lighter horn with an easier manufacturing technique in comparison with the other conventional horns, such as corrugated horns.

关键词： printed circuits surface impedance particle swarm optimisation satellite antennas antenna radiation patterns metamaterial antennas horn antennas side lobe level reduction pyramidal horn antenna gain enhancement low-refractive-index metasurface design method pyramidal horn antenna interior E-plane wall SLL reduction symmetric radiation patterns Ku band satellite communications particle swarm optimisation algorithm low-loss dispersion-engineered metasurfaces surface impedance characteristics low-cost printed circuit board technique metahorn field radiation patterns metasurface design approach frequency 13 GHz to 18 GHz gain 1 2 dB to 2 3 dB

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Hybrid shuffled frog leaping algorithm and Nelder-Mead simplex search for optimal reactive power dispatch

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第2期5卷 249-256页

作者： Khorsandi, A. Alimardani, A. Vahidi, B. Hosseinian, S. H. Amirkabir Univ Technol Dept Elect Engn Tehran Iran

The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder-Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

关键词： particle swarm optimisation transformer taps evolutionary algorithm non-linear mixed-variable optimisation problem IEEE 57-bus power systems optimisation techniques Nelder-Mead simplex search particle swarm optimisation algorithm IEEE 30-bus reactive power Transformers and reactors power transformers evolutionary computation optimal reactive power dispatch ORPD problem IEEE 118-bus power systems Nelder-Mead algorithm hybrid shuffled frog leaping algorithm Power systems

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Simultaneous Integrated stochastic electrical and thermal energy expansion planning

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第6期8卷 1017-1027页

作者： Abbasi, Ali Reza Seifi, Ali Reza Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

In this study, a stochastic multi-objective framework is proposed for energy expansion planning (EEP). The proposed multiobjective framework can concurrently optimise the competing objective functions including total real energy losses, voltage deviation and the total cost of the installation equipments. Also, regarding the uncertainties of the new complicated energy systems, in this study, for the first time, system uncertainties including load uncertainty are explicitly considered in the EEP problem by the use of the probabilistic load flow technique based on the point estimate method. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimise a single objective. Hence, the metaheuristic algorithm is applied to this problem. Here, the particle swarm optimisation (PSO) algorithm as a new evolutionary optimisation algorithm is utilised. To improve the total ability of the PSO for global search and exploration, a new modification adaptive process is suggested in such a way that the algorithm will search the total search space globally. To evaluate the feasibility and the effectiveness of the proposed algorithm, three modified standard distribution systems are used as the case studies.

关键词： load flow particle swarm optimisation power distribution planning thermal energy expansion planning integrated stochastic electrical energy expansion planning total real energy losses voltage deviation installation equipments. cost EEP problem probabilistic load flow technique metaheuristic algorithm particle swarm optimisation algorithm evolutionary optimisation algorithm modification adaptive process

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Optimal design of permanent magnet linear synchronous motors based on Taguchi method

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IET ELECTRIC POWER APPLICATIONS 2017年第1期11卷 41-48页

作者： Song, Juncai Dong, Fei Zhao, Jiwen Lu, Siliang Dou, Shaokun Wang, Hui Anhui Univ Dept Elect Engn Hefei 230601 Peoples R China

This study focuses on optimisation design of permanent magnet linear synchronous motors that are applied in laser engraving machines with no cutting force. Traditional analytical optimisation method based on magnetic field with particle swarm optimisation algorithm was introduced to obtain the best combination of motor structure parameters. By contrast, the novel optimisation design method - Taguchi method based on orthogonal array was proposed to optimise the thrust and thrust ripple. After the design of experiments using finite-element analysis, the relative importance of each design parameter was estimated in detail. Experimental results of prototype can certify the superiority and validity of Taguchi optimisation method.

关键词： linear synchronous motors permanent magnet motors Taguchi methods particle swarm optimisation magnetic fields finite element analysis permanent magnet linear synchronous motors Taguchi method optimal design laser engraving machines analytical optimisation method magnetic field particle swarm optimisation algorithm motor structure parameters optimisation design method orthogonal array thrust ripple finite-element analysis FEA relative importance

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High-accuracy localisation method for PD in transformers

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第1期14卷 104-110页

作者： Cai, Junyi Zhou, Lijun Hu, Junjie Zhang, Chenqingyu Liao, Wei Guo, Lei Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

This study proposes a high-accuracy method of localising partial discharge (PD) for transformer fault diagnosis. This study aims to solve the problem of high-accuracy estimation of PD in transformers by detecting the acoustic signals. First, by combining the advantages of the differential evolution (DE) algorithm and the particle swarm optimisation (PSO) algorithm, the authors describe a hybrid DE-PSO algorithm that can maintain great diversity even at the later stage of calculation. For further accuracy, a cooperative localisation differential evolution-particle swarm optimization-correction-Newton's method (DPCN) algorithm based on the DE-PSO algorithm and Newton's method with consideration of corrected time difference of arrival values is proposed. The results of simulations and experiments show that the proposed algorithm has excellent performance with high accuracy and strong robustness, and it can meet the needs of field applications.

关键词： fault diagnosis particle swarm optimisation partial discharges evolutionary computation transformers Newton method partial discharge PD transformer fault diagnosis high-accuracy estimation transformers acoustic signals differential evolution particle swarm optimisation algorithm hybrid DE-PSO algorithm localisation algorithm Newton's method high-accuracy localisation method high-accuracy method

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