scholarly journals Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 203
Author(s):  
Hu Wu ◽  
Xinning Li ◽  
Xianhai Yang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Dimensional Synthesis ◽  
Pareto Solution ◽  
Secondary Scattering ◽  
New Ideas ◽  
Reduce Energy Consumption ◽  
High Flexibility

The dimensional synthesis of multi-linkage robots has great significance for improving flexibility and efficiency. With the increase of the degree of freedom and restrictions on special occasions, the solution of dimensional synthesis becomes complicated and time-consuming. Theory of workspace density function, maneuverability, and energy expenditure had been studied. With high flexibility and low energy consumption as the design goal, the method for dimensional and joint angle synthesis of multi-linkage robots was proposed based on a niched Pareto genetic algorithm. The Pareto solution set has been obtained. The method was verified by two application examples, which is occlusion of the solar salt evaporation pool and the secondary scattering of solid 2,2′-azobis(2,4-dimethylvaleronitrile). Through the application of NPGA (niched Pareto genetic algorithm) compared with KPCA (kernel principal component analysis), it can save 12.37% time in occlusion of one evaporating pool and reduce energy consumption by 3.85%; it can save 9.96% time in scattering of remain materials per barrel and reduce energy consumption by 1.77%. The study reduces the labor intensity of manual workers in the salt making industry, ensures the safe production of dangerous chemicals, and provides new ideas and methods for the dimensional synthesis of multi-linkage robots.

scholarly journals Working and Limitations of Cable Stiffening in Flexible Link Manipulators

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Rahul Dixit ◽  
R. Prasanth Kumar
Keyword(s):  
Energy Consumption ◽  
Finite Number ◽  
Degrees Of Freedom ◽  
Control Algorithms ◽  
Flexible Link ◽  
The Past ◽  
Working Principle ◽  
Reduce Energy Consumption ◽  
High Flexibility ◽  
Special Control

Rigid link manipulators (RLMs) are used in industry to move and manipulate objects in their workspaces. Flexible link manipulators (FLMs), which are much lighter and hence highly flexible compared to RLMs, have been proposed in the past as means to reduce energy consumption and increase the speed of operation. Unlike RLM, an FLM has infinite degrees of freedom actuated by finite number of actuators. Due to high flexibility affecting the precision of operation, special control algorithms are required to make them usable. Recently, a method to stiffen FLMs using cables, without adding significant inertia or adversely affecting the advantages of FLMs, has been proposed as a possible solution in a preliminary work by the authors. An FLM stiffened using cables can use existing control algorithms designed for RLMs. In this paper we discuss in detail the working principle and limitations of cable stiffening for flexible link manipulators through simulations and experiments. A systematic way of deciding the location of cable attachments to the FLM is also presented. The main result of this paper is to show the advantage of adding a second pair of cables in reducing overall link deflections.

scholarly journals A Method for Short-Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G-ACO-BP Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guorong Sha ◽  
Qing Qian
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Bp Neural Network ◽  
Air Conditioning ◽  
Principal Component ◽  
Neural Model ◽  
Primary Energy ◽  
Kernel Principal Component Analysis ◽  
Short Term ◽  
Metro Station

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.

scholarly journals Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

2018 ◽  
Vol 10 (10) ◽  
pp. 3519 ◽  
Author(s):  
Cunrui Ma ◽  
Baohua Mao ◽  
Qi Xu ◽  
Guodong Hua ◽  
Sijia Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Vehicle Routing ◽  
Hazardous Materials ◽  
Transportation Energy ◽  
Pareto Solution ◽  
Hazardous Materials Transportation ◽  
Multi Objective ◽  
Transportation Risk ◽  
Multi Objective Genetic Algorithm

Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

scholarly journals Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yuyu Li ◽  
Wei Yang ◽  
Bo Huang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Environmental Sustainability ◽  
Mixed Integer ◽  
Environmental Aspects ◽  
Routing Problem ◽  
Vehicle Type ◽  
Aerial Vehicle ◽  
Reduce Energy Consumption

Compared with traditional vehicles delivery, unmanned aerial vehicle (UAV) delivery can reduce energy consumption and greenhouse gas emissions, which benefits environmental sustainability. Besides, UAVs can overcome traffic restrictions, which are the big obstacle in parcel delivery. In reality, there are two kinds of most popular traffic restrictions, vehicle-type restriction, and half-side traffic. We propose a mixed-integer (0-1 linear) green routing model with these two kinds of traffic restrictions for UAVs to exploit the environmental aspects of the use of UAVs in logistics. A genetic algorithm is proposed to efficiently solve the complex routing problem, and an experimental analysis is made to illustrate and validate our model and the algorithm. We found that, under both these two traffic restrictions, UAV delivery can accomplish deliveries that cannot be carried out or are carried out at much higher costs by vehicles only and can always effectively save costs and cut CO2 emissions, which is environmentally friendly. Furthermore, UAV delivery saves more cost and cuts more CO2 emission under the first kind of traffic restriction than that under the second.

Multi-objective optimization of all-wheel drive electric formula vehicle for performance and energy efficiency using evolutionary algorithms

2019 ◽  
Vol 234 (5) ◽  
pp. 1472-1479 ◽  
Author(s):  
Tey Jing Yuen ◽  
Rahizar Ramli
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Time Frame ◽  
Motor Power ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Multi Objective

A new method based on constraint multi-objective optimization using evolutionary algorithms is proposed to optimize the powertrain design of a battery electric formula vehicle with an all-wheel independent motor drive. The electric formula vehicle has a maximum combined motor power of 80 kW, which is a constraint for delivering maximum vehicle performance with minimal energy consumption. The performance of the vehicle will be simulated and measured against different driving events, that is, acceleration event, autocross event, and endurance event. Each event demands a different aspect of performance to be delivered by the motor. The respective event lap time or energy rating will be measured for performance assessment. In this study, a non-dominated sorting genetic algorithm II and constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution are employed to optimize the motor transmission ratio, motor torque scaling, and downforce scale of both front and rear wheels against the acceleration event to minimize energy consumption and event lap time while constraining the combined motor power of all wheels to not exceed 80 kW. The optimization will be performed through software-in-the-loop between MATLAB and VI-Grade, where the high-fidelity vehicle will be modeled in VI-Grade and optimization algorithms will be implemented on the host in MATLAB. Results show that the non-dominated sorting genetic algorithm II outperforms the constrained multi-objective evolutionary algorithm based on decomposition by using differential evolution in obtaining a wider distributed Pareto solution and converges at a relatively shorter time frame. The optimized results show a promising increase in the performance of the electric formula vehicle in completing those events with the highest combined performance scoring, that is, the lap time of acceleration events improves by 9.18%, that of autocross event improves by 6.1%, and that of endurance event improves by 4.97%, with minimum decrease in energy rating of 32.54%.

scholarly journals Research on Charging Combination Based on Batch Weight Fit Rule for Energy Saving in Forging

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zhu Baiqing ◽  
Lu Haixing ◽  
Tong Yifei ◽  
Li Dongbo ◽  
Xia Yong
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
High Energy ◽  
Forging Process ◽  
Proposed Model ◽  
Energy Consuming ◽  
Reduce Energy Consumption ◽  
Batch Weight ◽  
Holding Temperature

As a traditional high energy-consuming industry, the forging industry consumes a lot of energy. The activity consuming the highest energy during forging process is the heating. The problem regarding how to separate workpieces with the same holding temperature and holding time and combine them for charging in forging was analyzed and a model based on batch weight fit rule for optimizing the charging combination with the goal of energy saving was proposed. A genetic algorithm was adopted to optimize and solve the model in order to reduce energy consumption in forging. In addition, an instance was given to prove the effectiveness of the proposed model.

Naval Energy Management System

2020 ◽  
Vol 26 (3) ◽  
pp. 20-25
Author(s):  
Laurențiu Bogdan Asalomia ◽  
Gheorghe Samoilescu
Keyword(s):  
Energy Consumption ◽  
Power Systems ◽  
New Technologies ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Efficient Management ◽  
Reduce Energy Consumption ◽  
Energy Efficiency Design Index ◽  
Green Ship

AbstractThe paper analyses the role of control and monitoring of electro-energetic equipment in order to reduce operational costs, increase profits and reduce carbon emissions. The role of SCADA and EcoStruxure Power systems is presented and analysed taking into account the energy consumption and its savings. The paper presents practical and modern solutions to reduce energy consumption by up to 53%, mass by up to 47% and increase the life of the equipment by adjusting the electrical parameters. The Integrated Navigation System has allowed an automatic control and an efficient management. For ships, the implementation of an energy efficiency design index and new technologies was required for the GREEN SHIP project.

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