An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

2014 ◽  
Vol 96 (1) ◽  
pp. 49-56 ◽  
Author(s):  
V. Muthukumar ◽  
A. Suresh Babu ◽  
R. Venkatasamy ◽  
N. Senthil Kumar
Author(s):  
Ziyang Li ◽  
Quan Zhou ◽  
Yunfan Zhang ◽  
Ji Li ◽  
Hongming Xu

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.


Author(s):  
Yunfan Zhang ◽  
Quan Zhou ◽  
Ziyang Li ◽  
Ji Li ◽  
Hongming Xu

This article proposes an intelligent transient calibration method for the air-path controller of a light-duty diesel engine. This method is developed based on the chaos-enhanced accelerated particle swarm optimization algorithm. The target is to reduce the engine’s fuel consumption during transient scenarios by optimizing the controller parameters. The advanced dual-loop exhaust gas recirculation system is first introduced. Then, it formulates the transient calibration process as a multiple-objective optimization problem with constraints. Different from steady state calibration, the proposed method designs a new cost-function to evaluate the controller’s transient performance. The intelligent transient calibration module is programmed in MATLAB code. Interface between the calibration module and a physical engine plant is established via ETAS INCA. The optimization result of the proposal method is discussed by comparing it with the result of existing calibration methods. The engine performance with the calibrated controller is evaluated based on engine tests.


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