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Add-on integration module-based proportional-integration-derivative control for higher precision electro-optical tracking system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第6期43卷 1347-1362页

作者： Duan, Qianwen Mao, Yao Zhang, Hanwen Xue, Wenchao Chinese Acad Sci Key Lab Opt Engn Beijing Peoples R China Chinese Acad Sci Inst Opt & Elect Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS LSC Zhongguancongdonglu Rd 55 Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

This paper concerns the improvement on proportional-integration-derivative (PID) control for the electro-optical tracking system for high-mobility targets. To achieve higher tracking precision and stronger disturbance rejection while fully utilizing the existing PID loop, the add-on integration module is proposed and seamlessly integrated into the conventional PID loop. It is proven that for any given conventional PID controller parameters, the add-on integration module based PID control can improve the ability of error attenuation at low frequency and keep the stability of resulting closed-loop system. More importantly, the feasible set of all parameters in the added module is explicitly given. Based on the feasible set, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to obtain the globally optimal controller's parameter under certain performance indices. The experiments are carried out for a typical electro-optical tracking test bed with several reference signals. It is shown that the proposed method has much smaller tracking errors than the existing PID method.

关键词： Proportional-integration-derivative control electro-optical tracking system motion control disturbance rejection non-dominated sorting genetic algorithm

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A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm

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JOURNAL OF MANUFACTURING SYSTEMS 2021年第PartA期58卷 348-364页

作者： Liu, Zhengchao Wang, Lei Li, Xixing Pang, Shibao Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Ganzhou 341000 Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan 430068 Peoples R China

With the popularity of service-oriented manufacturing mode, the customer quantities of the online manufacturing service platforms are growing exponentially. To improve the user-friendliness and convenience of online platforms, the personalized service recommendation for different customer requirement is an effective means. However, since manufacturing services usually appear in the form of composite services, existing Web service-based personalized recommendation technologies are difficult to be applied effectively. Therefore, this paper proposes a novel hybrid algorithm to address the personalized recommendation for manufacturing service composition (MSC). The algorithm solves the insufficient individualization defect of MSC optimization by comprehensively considering the QoS objective attributes and customer preference attributes. First, a Clustering-based Collaborative Filtering (CCF) algorithm is proposed to quantify the customer preference attributes. Second, an improved Personalization-oriented third generation non-dominated sorting genetic algorithm (PoNSGA-III) is presented for the multi-attribute MSC optimization. Finally, the hybrid algorithm recommends the most suitable solutions for the target customer through the ranking of customer preference attributes. A detailed case study is designed to demonstrate the performance and practicability of the proposed recommendation algorithm.

关键词： Multi-attribute personalized recommendation Manufacturing service composition optimization Collaborative filtering non-dominated sorting genetic algorithm

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Investigation and Optimization of Surface Roughness and Material Removal Rate in Face Finishing Milling of Ti-6Al-4V Under MQL Condition

Investigation and Optimization of Surface Roughness and Mate...

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AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering, RCTEMME 2021

作者： Nguyen, Van-Canh Hoang Tien, Dung Pham, Van-Hung Nguyen, Thuy-Duong Faculty of Mechanical Engineering Hanoi University of Industry 298 Cau Dien Street Hanoi Viet Nam School of Mechanical Engineering Hanoi University of Science and Technology No.1 Dai Co Viet Street Hanoi Viet Nam

ISBN: (纸本)9789811919671

This experimental study presents a combination of Machine learning and Artificial Neural Network to solve a multiple objective optimization problem in finishing milling of Titanium Alloys Ti-6Al-4V under a minimum quantity lubrication (MQL) environment. The three milling technological parameters, such as feed per tooth, cutting speed and depth of cut;and two lubrication parameters including air pressure and lubricant flow rate are considered as the variants. The approach of this work is to minimize the cut value of surface roughness and maximize the production rate at the same time. The Support Vector Machine (SVM) is applied to generate the regression vectors, then the artificial neural algorithm non-dominated sorting genetic algorithm (NSGA II) is used to find the optimum technological and lubrication input. The ANOVA analysis is also used to predict the influence of input factors on surface roughness and production rate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Minimum quantity lubrication non-dominated sorting genetic algorithm Support vector machine Titanium alloy

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Form-finding method for the target configuration under dead load of a new type of spatial self-anchored hybrid cable-stayed suspension bridges

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ENGINEERING STRUCTURES 2021年 227卷 111407-111407页

作者： Wang, Xiaoming Wang, Huan Zhang, Ji Sun, Yuan Bai, Yunteng Zhang, Yufeng Wang, Haicheng Changan Univ Key Lab Transport Ind Bridge Detect Reinforcement Xian 710064 Shaanxi Peoples R China Suzhou Univ Sci & Technol Sch Civil Engn Suzhou 215011 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China Xian Highway Res Inst Xian 710065 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China

General form-finding problems of cable-supported bridges are established based on a design scenario in which rigidly fixed starting control points must be given as necessary design constraints prior to independent analysis of any of its cable subsystems. This article presents a form-finding method to address a new case, in which the starting control point serves as an intermediate, flexibly variable connection, to couple two related cable subsystems in a multi-nonlinear environment for the target configuration under dead load (TCUD) of a novel type of spatial self-anchored hybrid cable-stayed suspension (HCSS) bridge. A two-layer framework is proposed by integrating finite element analysis (FEA) and analytical formulas with optimization algorithms to form a self regulated interactive analysis among subsystems in an iterative manner. The outer layer seeks to achieve self equilibrium of the global system under the control information of the TCUD, while the inner layer optimizes the subsystems in terms of the initial tensions in the main cables, stay-cables, branches and hangers to obtain a rational mechanical state of the bridge. Then, the TCUD and the intermediate starting control points are determined. To achieve computational stability, a high-performance accelerated Steffens-Newton (ASN) differential algorithm is developed for the shape finding of the cable-hanger subsystem, whereas the non-dominated sorting genetic algorithm (NSGA-II) is adopted as a multiobjective optimizer for TCUD optimization of the other subsystems. The proposed framework is applied to a real-scale self-anchored HCSS bridge, and its validity and performance are demonstrated by comparison studies with a non-optimal scheme and in-field test data.

关键词： Form-finding analysis Self-anchored hybrid cable-stayed suspension bridge Cable force optimization Flexible components non-dominated sorting genetic algorithm Accelerated Steffens-Newton algorithm

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Bi-objective optimization for improving the locomotion performance of the vibration-driven robot

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ARCHIVE OF APPLIED MECHANICS 2021年第5期91卷 2073-2088页

作者： Diao, Binbin Zhang, Xiaoxu Fang, Hongbin Xu, Jian Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai 200092 Peoples R China Fudan Univ Inst AI & Robot Shanghai 200433 Peoples R China Fudan Univ Engn Res Ctr AI & Robot Minist Educ Shanghai 200433 Peoples R China Fudan Univ Shanghai Engn Res Ctr AI & Robot Shanghai 200433 Peoples R China

The average velocity and the backward velocity are two critical factors to evaluate the locomotion performance of a vibration-driven robot. These two factors are often antagonistic when the ground friction is isotropic and, consequently, lead to a bi-objective optimization. The goal of this paper is to give a practicable bi-objective optimization scheme for vibration-driven locomotion robots. To this end, a generalized dynamic model of the multi-module vibration-driven locomotion robot is built by assuming that the ground friction is isotropic Coulomb type and the driving force is ideal. The non-dominated sorting genetic algorithm II (NSGA-II) is then employed to construct the optimization scheme and then obtain the Pareto front. A numerical example with a two-module vibration-driven system is provided to demonstrate the necessity and effectiveness of applying the bi-objective optimization method. A robot prototype is further designed, and a series of experimental tests are carried out. By choosing the motors' rotary speed and phase shift as optimization parameters, the average and backward velocities are extracted to check against the Pareto front. The consistency between the theoretical predictions and the experimental results shows the practicability of the proposed bi-objective optimization scheme. Therefore, the proposed optimization scheme will provide comprehensive guidance for system design and optimization in micro-locomotion robots.

关键词： Vibration-driven locomotion system Micro-locomotion robot Isotropic Coulomb's dry friction non-dominated sorting genetic algorithm Pareto front

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Hypolimnetic aeration optimization based on reservoir thermal stratification simulation

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JOURNAL OF HYDROLOGY 2023年第PartB期625卷

作者： Mehdizadeh, Ghazal Nikoo, Mohammad Reza Talebbeydokhti, Nasser Vanda, Sadegh Nematollahi, Banafsheh Univ Nevada Desert Res Inst DRI Div Atmospher Sci Reno NV USA Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman Shiraz Univ Environm Res & Sustainable Dev Ctr Dept Civil & Environm Engn Shiraz Iran Shiraz Univ Dept Civil & Environm Engn Shiraz Iran

Hypolimnetic aeration is an effective management strategy for mitigating oxygen deficiency in dam reservoirs caused by thermal stratification (ThS). However, designing an optimal hypolimnetic aeration system based on dissolved oxygen (DO) concentration evaluation has yet to be considered in the literature. This study proposed an innovative optimization model utilizing the non-dominated sorting genetic algorithm-II (NSGA-II) for up to 4 aerators, considering two main objectives: (i) minimizing DO concentration deviation from the standard limit and (ii) minimizing aerator(s) operation energy. This optimization model was developed based on five primary decision variables: the location of the aerator(s) (length and depth), the aeration rate, and the operating time (start and end date). The NSGA-II optimization approach was coupled with the CE-QUAL-W2 simulation model for each identified period of ThS to simulate DO concentrations. To satisfy the conflicting objectives of the primary beneficiaries involving the Ministry of Energy and the Regional Water Organization, a two-step decision-making framework was introduced, utilizing the Nash bargaining (NB) and weighting method (WM) techniques. In order to show the practicality and effectiveness of the suggested framework in enhancing the DO concentration within a dam reservoir, the novel simulation optimization framework was implemented in a real-world case study of the Karkheh dam reservoir (KDR) in Iran. The findings revealed that applying an optimal hypo-limnetic aeration system significantly improved the DO concentration, reaching at least twice the level without an aerator. However, the influence of aerators was not long-lasting, gradually diminishing when their operations ceased. Considering the importance of increasing DO concentration to mitigate the adverse effects of ThS, the four-aerators system was chosen as the optimal compromise design for the KDR.

关键词： Associate Editor CE-QUAL-W2 simulation model Hypolimnetic aeration system design Nash bargaining non-dominated sorting genetic algorithm II Thermal stratification Weighting method

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Multi-objective optimization and dynamic response predictions of an articulated offshore wind turbine

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OCEAN ENGINEERING 2023年第1期273卷

作者： Zhang, Pei Li, Yan Tang, Yougang Zhang, Ruoyu Li, Haoran Gu, Jiayang Tianjin Univ Sch Civil Engn State Key Lab Hydraul Engn Simulat & Safety Tianjin 300350 Peoples R China Jiangsu Univ Sci & Technol Inst Marine Equipment Res Zhenjiang 212003 Jiangsu Peoples R China

In this manuscript, a compliant articulated offshore wind turbine (AOWT) is proposed to operate in the transitional water depth area and a three-objective optimization mathematical model is established to optimize the main dimensions of the articulated foundation. The relative optimal solutions (pareto solutions) are obtained by the third generation non-dominated sorting genetic algorithm (NSGA-III). The corresponding main dimensions are determined through the fuzzy comprehensive evaluation. In order to verify the robust dynamic performances of the optimized AOWT, the time domain simulations are carried out to numerically predict the AOWT responses under both rated operational and extreme survival sea scenarios. According to the results, the optimized AOWT is significantly improved in terms of construction cost, structural stability, motion performance and power generation stability, and fully satisfies the operating requirements under different sea scenarios. From the impact of different load factors on response results, the second-order load induces larger oscillations but has little effect on the mean positions of the AOWT. Both turbulent wind and second-order difference frequency second order force induce greater dynamic responses in the low-frequency region.

关键词： Articulated offshore wind turbine non-dominated sorting genetic algorithm Pareto solutions Optimization Dynamic response

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Facility layout design for modular construction manufacturing: A comparison based on simulation and optimization

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AUTOMATION IN CONSTRUCTION 2023年第1期147卷

作者： Yang, Zhongze Lu, Weisheng Univ Hong Kong Dept Real Estate & Construct Pokfulam Hong Kong Peoples R China

With modular construction reviving around the world, a significant portion of on-site works is now mass-produced as modules in factories, shifting Modular Construction Manufacturing (MCM) from a traditional oxymoron to an upfront challenge for project or production managers. Nevertheless, little is known about such MCM in a factory, e.g., the production line layout. This paper identifies the optimal Facility Layout Design (FLD) for MCM in a factory by simulating and optimizing a real-life case. Firstly, the workflows and schedules of MCM are collected and re-engineered into nine categories. Then, data is collected and implemented to five candidate FLDs for simulation and optimization. The results show that 'cellular layout' has the most output and 'product layout' is the most economical selection in the case factory. This research sheds light on the uncharted area of MCM. It also calls for more studies to examine Industry 4.0 advances in transforming the traditional construction industry.

关键词： Modular construction Offsite construction Facility layout design Simulation-based comparison Multi-objective optimization non-dominated sorting genetic algorithm

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Construction scheduling optimization in the prefabricated buildings through multi-objective GA on the principle of sustainable development

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ARCHIVES OF CIVIL ENGINEERING 2024年第3期70卷 459-472页

作者： Yang, Chuanguang Open Univ Shaanxi Coll Architecture & Engn Xian 710000 Peoples R China

To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of traditional buildings is mostly focused on the control of on-site resources, which cannot scientifically and reasonably complete the construction goal of prefabricated building. In response to the above issues, a resource constrained scheduling model based on genetic algorithm is designed by sustainable development, and an improved non dominated sorting genetic algorithm with elite strategy is introduced. It is used to solve the time cost weight balance scheduling model and the low-cost low-carbon scheduling model. The research results indicated that this algorithm had a reverse generation distance value of 0.35 when evaluated 4000 times, and a super volume value of 0.43 when evaluated 10000 times. In the application of a certain affordable housing project, the resource constrained scheduling model based on genetic algorithm can shorten the assembly phase to 8 days, and the low-cost low-carbon scheduling model using proposed algorithm can reduce the transportation cost and carbon emission duration of transportation vehicles to 22501 yuan and 93.75 h, respectively. Resource constrained scheduling models based on genetic algorithms and low-cost low-carbon scheduling models have potential in the field of green buildings, which can achieve significant results in saving time, cost, and reducing carbon emissions. These research results can provide reference for the promotion and practice of green buildings, and guide the formulation and implementation of relevant policies.

关键词： non-dominated sorting genetic algorithm prefabricated building prefabricated component resource constraints sustainable development principles

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Pareto front generation for integrated drive-train and structural optimisation of a robot manipulator conceptual design via NSGA-II

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ADVANCES IN MECHANICAL ENGINEERING 2023年第3期15卷

作者： Gulec, Musa Ozgun Ertugrul, Seniz Istanbul Tech Univ Mech Engn Dept Istanbul Turkiye Izmir Univ Econ Mechatron Engn Dept Izmir Turkiye Istanbul Tech Univ Mech Engn Dept Gumussuyu Inonu Cd TR-34437 Istanbul Turkiye

Due to the complexity of the process, there is no single solution for determining the motors, gearboxes and structures of a robot manipulator according to the desired dynamic performance while minimising both the deflections in the structure during the dynamic motion and total robot weight. The solution of this integrated drive-train and dynamic structural optimisation problem is generalised for three degrees of freedom (DOF) robot manipulator via non-dominated sorting genetic algorithm II (NSGA-II) to obtain the Pareto front of any desired robot manipulator overall conceptual design, including motors, gearboxes and thicknesses of the links. A flexible body dynamic simulation model was created in the MATLAB Simmechanics environment. The flexible bodies were defined via lumped parameter estimation method, which allows observation of the deflections in links during the dynamic motion. A library containing technical data related to motors and gearboxes was created to be utilised in the optimisation algorithm. The method accelerates the time-consuming iterative process for obtaining optimum conceptual design solutions for a dynamic system and allows for easy modification of design parameters and constraints. It also makes the algorithm suitable for different types of dynamic system designs.

关键词： Robot design integrated conceptual design optimisation dynamic simulation of flexible bodies the lumped parameter estimation drive-train optimisation non-dominated sorting genetic algorithm

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