Variable-Gain Cross Coupling Control for Linear Motor X-Y Table Based on Genetic Algorithm

2011 ◽  
Vol 383-390 ◽  
pp. 7104-7110
Author(s):  
Li Mei Wang ◽  
Yue Pang
Keyword(s):  
Genetic Algorithm ◽  
Cross Coupling ◽  
Contour Error ◽  
Local Optimum ◽  
Variable Gain ◽  
Optimal Value ◽  
Cross Coupling Control ◽  
Global Optimal ◽  
Contour Accuracy ◽  
Coupling Error

To achieve high precision contour tracking control for Permanent Magnet Linear Synchronous motor drive X-Y table, genetic algorithm was used on PID controller parameters optimized. By the ability of gene mutation, the parameters were avoided falling into local optimum point. Consequently, the global optimal value can be found directly. Variable-gain cross coupling controller was used to compensate X-Y axis coupling error. Eventually the path tracking contour error is greatly reduced to the expected accuracy limits. Theoretical analysis and simulation results show that the designed control system make X-Y table possess high contour accuracy

Cross-Coupling Controller for Five Degree-of-Freedom Coplanar Nanostage

2013 ◽  
Vol 284-287 ◽  
pp. 1788-1793
Author(s):  
Van Tsai Liu
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Cross Coupling ◽  
Degree Of Freedom ◽  
Creep Model ◽  
Contour Error ◽  
Viscoelastic Creep ◽  
Control Scheme ◽  
Optimization Parameters ◽  
Cross Coupling Control

The proposed approach is to design a tracking controller for five degree-of-freedom coplanar nanostage which can provide high precision applications. This study propose a viscoelastic creep model, it was modeled as a series connection of springs and dampers to describe the creep effect. Then, utilize a PI controller using Taguchi method to search the optimization parameters to suppress the tracking error. Finally, a cross-coupling control scheme is proposed to eliminate the contour error which is typical in dual-axes tracking control problem. The developed approaches are numerically and experimentally verified which demonstrate performance and applicability.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 588-591
Author(s):  
Yan Li Yang ◽  
Wei Wei Ke
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Local Optimum ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Global Optimal ◽  
Simulation Results

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

Successive Over-Relaxation Method Based on PSO

2015 ◽  
Vol 734 ◽  
pp. 522-525 ◽  
Author(s):  
Liang Tang ◽  
Zhi Chao Wang ◽  
Lei Gao
Keyword(s):  
Genetic Algorithm ◽  
Linear Equations ◽  
Relaxation Method ◽  
Pso Algorithm ◽  
Operational Quality ◽  
Optimal Value ◽  
Global Aspect ◽  
Global Optimal ◽  
Successive Over Relaxation ◽  
Search Quality

To solve large linear equations using SOR method, the most important thing is to ascertain relaxation factor. Considering current methods can not get the factor from global aspect, iteration times become larger and speed become slower. We pose a method to fix optimal factor using global search quality, genetic operational quality and compare the factor value obtaining from PSO algorithm and genetic algorithm, parabolic method. As a result, it shows that it is easier for PSO method to get optimal value than genetic and parabolic method from simulation result. PSO algorithm has huge advantage on solving global optimal problems. It is definite that PSO algorithm has great advantage then other methods and this method, and another advantage is it’s feasibility and convenience.

New Cross-Coupling Control of Independent Linear Contour Errors Based on Backlash

2010 ◽  
Vol 663-665 ◽  
pp. 902-905
Author(s):  
Sheng Bao Wang ◽  
Xiao Hong Liu
Keyword(s):  
Error Control ◽  
Cross Coupling ◽  
Contour Error ◽  
Contour Tracking ◽  
Control Scheme ◽  
New Strategy ◽  
Cross Coupling Control ◽  
Simulation Results ◽  
Contour Machining ◽  
Contour Error Control

On the basis of analysis of linear contour errors model, a new strategy of independent contour error control was presented for high precision contour machining. The proposed control scheme, in which the equations of the well-known cross-coupling controller were implemented, is shown to be able to diminish the linear contour error without using any cross-feeding signals between the driving axes. The simulation results show that the proposed control scheme is effective and that better effect of the contour tracking can be obtained. As a result, the contour machining precision is improved greatly.

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