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Chinese Automation Congress (CAC)

作者： Zhang, Chunyu Wang, Liwei Yu, Xinghu Tong, Mingsi Lin, Weiyang Gao, Huijun Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China Harbin Inst Technol Inst Intelligent Control & Syst Harbin Peoples R China HIT Ningbo Inst Intelligent Equipment Technol Ningbo Peoples R China

ISBN: (纸本)9781728176871

This article mainly designs a trajectory lanning algorithm for DELTA parallel robots. This algorithm is a timeoptimal trajectory planning algorithm. The space trajectory is based on the NURES Curves, that is, the curve equation is calculated after wo know the positions which the robot pass through in the space. Then use the genetic optimization algorithm to optimize the curve, get the optimized curve and then discretize the curve for interpolation calculation. At last we use the V-rep simulation software to simulate the algorithm, the similarity between the simulation trajectory and the calculation trajectory is very well, and the experiment proves that the algorithm is effective.

关键词： NURBS Curves formatting genetic optimization algorithm V-rep simulation time-optimal trajectory planning algorithm

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Experimental investigation of the strength of joining dissimilar aluminum alloys by friction stir welding and optimization of effective parameters with artificial intelligence

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Hosein Rostami Saeid Nickabadi Elyas Rostami Kave Yazdi PhD. Student of Mechanical Engineering Babol Noshirvani University of Technology Babol Iran Faculty of Mechanical Engineering University of Imam Khomeini Marine Sciences Nowshahr Iran Faculty of Aerospace Engineering Buein Zahra Technical and Engineering University Buein Zahra Iran

In this study, the friction stir welding process has been investigated for joining dissimilar 6061 aluminum alloy to 5083 aluminum alloy. Cutting and cleaning of the surface of the samples have been performed for friction stir welding. By evaluating the feasibility of friction stir welding of dissimilar 6061 and 5083 aluminum alloys to each other and welding them, the samples were examined to investigate mechanical properties. Tensile testing was used to investigate mechanical properties. After performing mechanical property tests, optimization of friction stir welding process parameters on tensile strength and weld yield strength was carried out with artificial intelligence. Response surface methodology, genetic algorithms, and particle swarm optimization algorithms have been used to optimize welding parameters. The results of the tensile test showed an improvement in the ultimate strength and yield strength of welded samples at a tool rotational speed of 800 rpm and a tool traverse speed (welding speed) of 100 mm/min with an ultimate tensile strength of 199 MPa. The effect of welding parameters such as tool rotational speed and tool traverse speed has also been investigated through optimization. The optimal process parameters to maximize tensile strength and yield strength were obtained at a rotational speed of 630 rpm and a tool welding speed of 128 mm/min. The maximum yield strength in the weld zone for the combination of the optimal parameters of the process was obtained at 126 MPa, and the maximum tensile strength in the weld zone was obtained at 172 MPa.

关键词： Friction stir welding 6061 and 5083 aluminum alloys Response surface optimization algorithm genetic optimization algorithm Particle swarm optimization algorithm

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Numerical and experimental transition results evaluation for a morphing wing and aileron system

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AERONAUTICAL JOURNAL 2018年第1251期122卷 747-784页

作者： Botez, R. M. Koreanschi, A. Gabor, O. S. Tondji, Y. Guezguez, M. Kammegne, J. T. Grigorie, L. T. Sandu, D. Mebarki, Y. Mamou, M. Amoroso, F. Pecora, R. Lecce, L. Amendola, G. Dimino, I. Concilio, A. Ecole Technol Super Res Lab Act Control Avion & AeroServoElast LARCASE Montreal PQ Canada NRC Aerosp Aerodynam Lab Ottawa ON Canada Univ Naples Federico II Ind Engn Dept Aerosp Div Naples Italy Italian Aerosp Res Ctr CIRA Adapt Struct Div Capua CE Italy

A new wing-tip concept with morphing upper surface and interchangeable conventional and morphing ailerons was designed, manufactured, bench and wind-tunnel tested. The development of this wing-tip model was performed in the frame of an international CRIAQ project, and the purpose was to demonstrate the wing upper surface and aileron morphing capabilities in improving the wing-tip aerodynamic performances. During numerical optimisation with 'in-house' genetic algorithm software, and during wind-tunnel experimental tests, it was demonstrated that the air-flow laminarity over the wing skin was promoted, and the laminar flow was extended with up to 9% of the chord. Drag coefficient reduction of up to 9% was obtained when the morphing aileron was introduced.

关键词： aerodynamics Morphing Wing Tip genetic optimization algorithm Flow Transition Drag Coefficient Reduction Wind Tunnel Tests Morphing Aileron Classification Description: Aerodynamics Aeroelasticity Avionics and Systems Computational Fluid Dynamics Experimental Fluid Dynamics

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Intelligent algorithm for energy, both thermal and electrical, economic and ecological optimization for a smart building

Intelligent algorithm for energy, both thermal and electrica...

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IEEE International Energy Conference (ENERGYCON)

作者： Favre, Ludovic Schafer, Thibaut M. Robyr, Jean-Luc Niederhauser, Elena-Lavinia Univ Appl Sci Western Switzerland HES SO Fribourg ENERGY Inst Fribourg Switzerland

ISBN: (纸本)9781538636695

In the frame of a research project conducted at the Smart Living Lab (SLL), a research center dedicated to the building of the future, this paper presents an algorithm that optimizes the coupling of local renewable energy production systems with energy storage devices and the different consumers both at the level of the building and of its peripherals. The main goal is to improve the energy self-sufficiency of a building by combining three aspects in the same time. The optimization criteria are the renewable energy based independence and the ecological (greenhouses gases emissions) and economical (costs) aspects. The underlying approach to perform the global optimization is first presented, explaining how the algorithm combines and optimizes these three criteria. For this purpose, it takes into account the current value of the state variables (temperature, etc.) and the forecasts future values. These data represent the input of a genetic optimization algorithm that computes the best use of each element of the production and storage systems to ensure the electrical and thermal energy demand. The choice of genetic algorithm is motivated by the large amount of optimization variables and the non-linearity of the score function. The typical computation time for this kind of optimization is short enough to allow a real time regulation. The composition of the energy production and storage is flexible allowing to integrate many technologies types, thus increasing its portability.

关键词： Energy building control energy management thermal modelling simulation genetic optimization algorithm renewable greenhouse gases CO2 electricity storage mobility self-consumption self-production

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A New Procedure for Tuning an Allocator and Designing a Robust High-Level Control Law for Over-Actuated Systems.

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IFAC-PapersOnLine 2017年第1期50卷 12741-12747页

作者： Evain, Hélène Rognant, Mathieu Alazard, Daniel Mignot, Jean Ph.D student ONERA Toulouse France Research Scientist ONERA Toulouse France ISAE SUPAERO Toulouse France AOCS Senior Expert CNES Toulouse France

This paper presents a new integrated procedure to tune a control law for over-actuated mechanical systems that may encounter singularities. First, the allocator that divides the commands among the actuators is tuned thanks to a genetic optimization algorithm, that computes the optimal values of its parameters. Then, the open-loop system including the allocator is identified and a robust closed-loop controller is computed with the structured H∞method. Indeed, near singularities, the system and the allocator may create errors to deviate from these points or create delays to reconfigure the actuators, hence there is a need to create a closed-loop controller robust to these characteristics and to parameter variations. This procedure is carried out on a planar redundant robotic manipulator example. Simulations of the behaviours of the open-loop system with the allocator and in a closed loop are presented. © 2017

关键词： Controllers Actuators Control engineering Control theory Electronic guidance systems Flexible manipulators Machine design Navigation Robot applications Robots Allocation Closed loop controllers Design Methodology genetic optimization algorithm High level control Overactuated systems Robot manipulator Robotic manipulators

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Permanent Magnet Generator for Counter-Rotating Vertical Axis Micro-Wind Turbine 52

Permanent Magnet Generator for Counter-Rotating Vertical Axi...

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52nd International Universities Power Engineering Conference (UPEC)

作者： Dranca, Marius Chirca, Mihai Zaharia, Valentin Zaharia, Andreea Breban, Stefan Tech Univ Cluj Napoca Dept Elect Machines & Drives Cluj Napoca Romania

This paper approaches the design using two optimization techniques for a permanent magnet generator used in a micro-wind turbine, with a major importance on the overall performances of the energy conversion system. Th... 详细信息

ISBN: (纸本)9781538623442

关键词： Direct drive finite element analysis genetic optimization algorithm permanent magnet generator space mapping optimization technique wind energy conversion systems

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Prediction of the roof weighting using the parameters back-analysis method combined with genetic algorithms

Prediction of the roof weighting using the parameters back-a...

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36届国际采矿岩层控制会议

作者： CHEN Yue-du LIANG Wei-guo YANG Jian-feng LIAN Hao-jie Ning-Xiao College of Mining Engineering Taiyuan University of Technology Taiyuan 030024 China Key Laborator College of Mining Engineering Taiyuan University of Technology Taiyuan 030024 China

Owing to the many unknown parameters and complex overlying stratums structure evolution, the back analysis of the dynamic deformation is becoming a typical complex nonlinear optimization *** order to overcome the low efficiency of the conventional inversion method in multi-parameters back analysis,firstly, using the strata control theory to analyze the dynamic structure evolution of overlying stratums, and choosing the global search optimization algorithm-genetic algorithm,besides combining the implicit nonlinear relationship between supporting resistance and the multi parameters of the overlying rock to find the minimum between field measured value and numerical calculation values, and the unknown lithologic parameters were determined when the minimum was *** these unknown parameters, including the rock mass density, mechanical parameters—elasticity modulus and poisson ratio, and strength parameters—cohesion and internal friction ***, choosing the No.8206 working face in Qingciyao coal mine as the testing site,the hard stratum fracture overlying which always lead to the rapidly increase of supports resistance, and combining the hydraulic supporting resistance value variation with time, and using the algorithm to acquire the optimal parameters and instability rules of the main roof, and the simulation results show a good agreement with the measured ***, in this study, using the optimal numerical model can well analyze the fracture characteristic of the overlying stratums and the variation of the hydraulic supports with the different advance distances.

关键词： genetic optimization algorithm overlying stratum deformation unknown parameters back analysis surrounding rock mass instability prediction technology

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Non-ideal step response identification modeling algorithm of the second-order delayed system 12

Non-ideal step response identification modeling algorithm of...

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12th IEEE International Conference on Electronic Measurement and Instruments (ICEMI)

作者： Liu Weidong Mech Engn Coll Inst Electrostat & Electromagnet Protect Shijiazhuang 050003 Hebei Peoples R China

ISBN: (纸本)9781479970711

Ideal step signal is nonexistent in practical application and the input signals used in system identification are actually not ideal step signals. This introduced errors to the direct identification modeling method that based on the ideal step input assumption. Therefore, an improved algorithm based on non-ideal step signal input was proposed in this paper. A parameter represent the time of the non-ideal step signal was introduced in this algorithm, and it can be decided automatically during the model identification by using genetic optimization algorithm (GA), which means that this proposed identification modeling algorithm has higher precision and also can be used directly in the practical application. Time effectiveness and applicability of this method has been demonstrated through the simulation model identification under the non-ideal step signal input.

关键词： System identification Continuous domain Second-order system Non-ideal step signal genetic optimization algorithm

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genetic optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

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INTERNATIONAL JOURNAL OF CHEMICAL REACTOR ENGINEERING 2014年第1期12卷 205-214页

作者： Chen, Xi Zhong, Wenqi Wang, Tiancai Liu, Fei Zhang, Zhi Southeast Univ Key Lab Energy Thermal Convers & Control Minist Educ Sch Energy & Environm Nanjing 210096 Jiangsu Peoples R China Nanjing Iron & Steel Grp Corp Nanjing 210035 Jiangsu Peoples R China

Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

关键词： genetic optimization algorithm support vector machine (SVM) pellet shaft furnace

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A Modified Model with genetic optimization algorithm for Land Evaluation

A Modified Model with Genetic Optimization Algorithm for Lan...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Miao Zuohua Liu Yanzhong Chen Yong Zeng Xiangyang Wuhan Univ Sci & Technol Sch Resource & Environm Engn Wuhan Peoples R China

ISBN: (纸本)9780769538167

Trained speed of model based on traditional BP neural network was slowly and produced emanative result. A novel land evaluation model based on neural network with genetic optimization algorithm was presented in this paper. The neural network of model is front-network which comprised with five layers architecture which composed of dynamic inference with fuzzy rules where the consequent sub-models are implemented by recurrent neural networks. The recurrent neural networks with internal feedback paths and dynamic neuron synapses. In order to optimized the parameter structure and link weight between layers, the author adopted genetic algorithm into model. Experiment results demonstrated that the novel model exhibit superior performance such as enhanced representation power, calculation speed and veracity of result than traditional BP neural network and the other land evaluation models.

关键词： BP neural network land evaluation model genetic optimization algorithm

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