Optimal Design and Control of a Slider-Crank Mechanism System

2012 ◽  
Vol 487 ◽  
pp. 608-612 ◽  
Author(s):  
Chih Cheng Kao
Keyword(s):  
Particle Swarm Optimization ◽  
System Identification ◽  
Optimal Design ◽  
Fitness Function ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Pm Synchronous Motor ◽  
And Control ◽  
Crank Mechanism

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.

scholarly journals A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

Kursor ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 33
Author(s):  
Alrijadjis Alrijadjis
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Inertia Weight ◽  
Modified Pso

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia weight adjustment, the modified PSO paradigm is named Modified PSO with random activation (MPSO-RA). The experiments with three famous benchmark functions show that the accuracy and success rate of the proposed MPSO-RA increase of 43.23% and 32.95% compared with the standard PSO.

scholarly journals Condition monitoring of Induction motors through Simulation of Bearing Fault and Air Gap Eccentricity Fault

2019 ◽  
Vol 8 (3) ◽  
pp. 176-193
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motors ◽  
Fitness Function ◽  
Particle Swarm ◽  
Air Gap ◽  
Percentage Error ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Modified Particle Swarm Optimization ◽  
Comparison Results

Induction motors have an important role in the industry on account of their advantages over other electrical motors. Consequently, there is a huge demand for their safe and sound operation. But it is not free from failures, which result in unnecessary downtimes and create great losses with regards to both revenue and maintenance. For that reason, early fault detection is considered necessary for the safety maintenance of the motor. In the present circumstances, the health monitoring of the induction motors are progressively increasing due to its potential to enhance operating costs, increase the reliability of function and so does the current paper emerge. Also, this paper deals with a novel effective technique for detecting the bearing fault and air gap eccentricity fault of the induction motor. Summarization and analysis of the findings are done based on percentage error and fitness function Value. Comparison results of bad bearing faults and air gap eccentricity are given separately in the paper. The findings of the study concluded that particle swarm optimization (PSO) can be considered as better optimization for bad bearing fault whereas modified particle swarm optimization is concluded as better optimization for air gap eccentricity fault.

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

2014 ◽  
Vol 912-914 ◽  
pp. 1138-1141 ◽  
Author(s):  
Ning Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Chance Constrained Programming ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Complex Calculation ◽  
Long Time ◽  
Hybrid Intelligence ◽  
Constrained Programming ◽  
Stochastic Particle

Stochastic chance-constrained programming which is one of important stochastic programming widely exits in different fields. For searching an algorithm that can more effectively solve this problem,a new algorithm for its combined stochastic particle swarm optimization with stochastic simulation for approximation of the fitness function and checking feasibility of solution is presented. It overcomes the defaults such as needing a long time, complex calculation,resapsing into local optimum in the hybrid intelligence algorithm based on GA. After testing its performance and comparing with GA, the results show that the algorithm is more preferable.

scholarly journals Jiles-Atherton Based Hysteresis Identification of Shape Memory Alloy-Actuating Compliant Mechanism via Modified Particle Swarm Optimization Algorithm

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Le Chen ◽  
Ying Feng ◽  
Rui Li ◽  
Xinkai Chen ◽  
Hui Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Compliant Mechanism ◽  
Particle Swarm ◽  
Local Optimum ◽  
Identification Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Compliant Actuator

Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.

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