Force control approaches research for robotic machining based on particle swarm optimization and adaptive iteration algorithms

2018 ◽  
Vol 45 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Shouyan Chen ◽  
Tie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Machining Process ◽  
Machining Parameters ◽  
Control Parameters ◽  
Pd Control ◽  
Swarm Optimization ◽  
Content Type ◽  
Robotic Machining ◽  
Intelligent Approach

Purpose The purpose of this paper is to reduce the strain and vibration during robotic machining. Design/methodology/approach An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state. Findings The proposed intelligent approach can significantly reduce robotic machining strain and vibration. Originality value The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

scholarly journals Multi response Particle Swarm Optimization of Wire Electro Discharge Machining parameters of Nitinol alloys

2020 ◽  
Author(s):  
Mohammed Yunus ◽  
Mohammad S. Alsoufi
Keyword(s):  
Particle Swarm Optimization ◽  
Metal Removal ◽  
Removal Rate ◽  
Particle Swarm ◽  
Optimization Method ◽  
Machining Parameters ◽  
Swarm Optimization ◽  
Electro Discharge Machining ◽  
Process Factors ◽  
Wedm Process

Abstract The conventional process of Machining of Nitinol alloy leads to extensive wear on the tool and deprived surface quality. Wire electro discharge machining (WEDM) is widely accepted for machining this alloy involving various input factors, namely, P, (pulse-on-duration), Q, (pulse-off-duration), C, (maximum-current), and V, (voltage). The factor’s effect on MRR (metal removal rate) and SR (surface roughness) responses and multi-response optimization of the WEDM process by employing PSO (particle swarm optimization) method are studied. The relationship model between factors and response characteristics were generated by ANOVA and optimized by response surface methodology has shown more significant factors (A and C). Though the effect of WEDM process factors on SR and MRR are contradictory when studied individually. MRPSO method was employed to get the best optimum condition for minimizing SR and maximizing MRR. MRPSO results improved the responses for vast combination of optimal setting of factors to meet the product requirements.

Comparative analysis of lunar capture braking method based on particle swarm optimization

2019 ◽  
Vol 91 (4) ◽  
pp. 558-566
Author(s):  
Chengchao Bai ◽  
Jifeng Guo ◽  
Wenyuan Zhang ◽  
Tianhang Liu ◽  
Linli Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Fuel Consumption ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Lunar Exploration ◽  
Swarm Optimization ◽  
Capture Process ◽  
Content Type ◽  
Advantages And Disadvantages

Purpose The purpose of this paper is to verify the feasibility of lunar capture braking through three methods based on particle swarm optimization (PSO) and compare the advantages and disadvantages of the three strategies by analyzing the results of the simulation. Design/methodology/approach The paper proposes three methods to verify capture braking based on PSO. The constraints of the method are the final lunar orbit eccentricity and the height of the final orbit around the Moon. At the same time, fuel consumption is used as a performance indicator. Then, the PSO algorithm is used to optimize the track of the capture process and simulate the entire capture braking process. Findings The three proposed braking strategies under the framework of PSO algorithm are very effective for solving the problem of lunar capture braking. The simulation results show that the orbit in the opposite direction of the trajectory has the most serious attenuation at perilune, and it should consume the least amount of fuel in theoretical analysis. The methods based on the fixed thrust direction braking and thrust uniform rotation braking can better ensure the final perilune control accuracy and fuel consumption. As for practice, the fixed thrust direction braking method is better realized among the three strategies. Research limitations/implications The process of lunar capture is a complicated process, involving effective coordination between multiple subsystems. In this article, the main focus is on the correctness of the algorithm, and a simplified dynamic model is adopted. At the same time, because the capture time is short, the lunar curvature can be omitted. Furthermore, to better compare the pros and cons of different braking modes, some influence factors and perturbative forces are not considered, such as the Earth’s flatness, light pressure and system noise and errors. Practical implications This paper presents three braking strategies that can satisfy all the constraints well and optimize the fuel consumption to make the lunar capture more effective. The results of comparative analysis demonstrate that the three strategies have their own superiority, and the fixed thrust direction braking is beneficial to engineering realization and has certain engineering practicability, which can also provide reference for lunar exploration orbit design. Originality/value The proposed capture braking strategies based on PSO enable effective capture of the lunar module. During the lunar exploration, the capture braking phase determines whether the mission will be successful or not, and it is essential to control fuel consumption on the premise of accuracy. The three methods in this paper can be used to provide a study reference for the optimization of lunar capture braking.

ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

2019 ◽  
Vol 40 (2) ◽  
pp. 235-247
Author(s):  
Asma Ayari ◽  
Sadok Bouamama
Keyword(s):  
Particle Swarm Optimization ◽  
State Space ◽  
Task Allocation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Content Type ◽  
Automatic Clustering ◽  
Run Time ◽  
Robot Task ◽  
Multi Robot

Purpose The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO. Design/methodology/approach This approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid. Findings Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time. Practical implications The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks. Originality/value In this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

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