基于自适应PSO优化的空燃比神经网络预测控制

西南师范大学学报(自然科学版)

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基于自适应PSO优化的空燃比神经网络预测控制

基于自适应PSO优化的空燃比神经网络预测控制[J]. 西南师范大学学报(自然科学版), 2014, 39(5).

引用本文:

基于自适应PSO优化的空燃比神经网络预测控制[J]. 西南师范大学学报(自然科学版), 2014, 39(5).

Air Fuel Ratio Based on Adaptive Particle Swarm Optimization Neural Network Predictive Control[J]. Journal of Southwest China Normal University(Natural Science Edition), 2014, 39(5).

Citation:

Air Fuel Ratio Based on Adaptive Particle Swarm Optimization Neural Network Predictive Control[J]. Journal of Southwest China Normal University(Natural Science Edition), 2014, 39(5).

基于自适应PSO优化的空燃比神经网络预测控制

Air Fuel Ratio Based on Adaptive Particle Swarm Optimization Neural Network Predictive Control

摘要: 为解决空燃比传输延迟的问题，该文提出一种基于自适应扩展粒子群优化的空燃比预测控制策略。采用多粒子策略来提高算法的全局收敛性，通过对控制参数的自适应调整来加快算法的收敛速度。在多粒子策略中，每个粒子的更新受更多其他粒子的影响；在自适应策略中，控制参数随着迭代次数的增加而逐渐减小。以 HQ495发动机为实验对象，仿真结果表明在节气门小范围变化时，空燃比误差低于1％；在节气门大范围变化时，空燃比误差低于2％。该方法实现了对空燃比的精确预测控制，有效地改善了汽油机过渡工况排放性能。
- 空燃比 /
- 自适应粒子群优化 /
- 神经网络 /
- 预测控制 /
- 过渡工况
Abstract: In order to solve problem of the transmission delay of air‐fuel ratio ,an air‐predictive control strategy based on adaptive extended PSO has been proposed in this paper .The global convergence of the algorithm has been improved by adopting multi‐particle strategy ,the convergence speed of the algorithm is also accelerated through controlling the self‐adaptive adjustment of Parameter .In the multi‐particle strate‐gy ,the update of each particle is affected by the other more particles ;in the self‐adaptive strategy ,the control parameter decreases gradually with the increase of iterations .Engine of HQ495 has been chosen as the experimental object ,and the simulation results show that ,w hen the throttle changes small ,the error of air‐fuel ratio will be less than 1% ,w hile the throttle experiences a wide range of changes ,the error of air‐fuel ratio will be less than 2% .The method realizes an accurate predictive control to air‐fuel ratio and effectively improves the emissions performance of gasoline engines under transient operation .
- air-fuel ratio optimization of self-adaptive particle swarm neural network predictive control transient operation /

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基于自适应PSO优化的空燃比神经网络预测控制

关键词:

摘要: 为解决空燃比传输延迟的问题，该文提出一种基于自适应扩展粒子群优化的空燃比预测控制策略。采用多粒子策略来提高算法的全局收敛性，通过对控制参数的自适应调整来加快算法的收敛速度。在多粒子策略中，每个粒子的更新受更多其他粒子的影响；在自适应策略中，控制参数随着迭代次数的增加而逐渐减小。以 HQ495发动机为实验对象，仿真结果表明在节气门小范围变化时，空燃比误差低于1％；在节气门大范围变化时，空燃比误差低于2％。该方法实现了对空燃比的精确预测控制，有效地改善了汽油机过渡工况排放性能。

English Abstract

Air Fuel Ratio Based on Adaptive Particle Swarm Optimization Neural Network Predictive Control

Keywords:

Abstract: In order to solve problem of the transmission delay of air‐fuel ratio ,an air‐predictive control strategy based on adaptive extended PSO has been proposed in this paper .The global convergence of the algorithm has been improved by adopting multi‐particle strategy ,the convergence speed of the algorithm is also accelerated through controlling the self‐adaptive adjustment of Parameter .In the multi‐particle strate‐gy ,the update of each particle is affected by the other more particles ;in the self‐adaptive strategy ,the control parameter decreases gradually with the increase of iterations .Engine of HQ495 has been chosen as the experimental object ,and the simulation results show that ,w hen the throttle changes small ,the error of air‐fuel ratio will be less than 1% ,w hile the throttle experiences a wide range of changes ,the error of air‐fuel ratio will be less than 2% .The method realizes an accurate predictive control to air‐fuel ratio and effectively improves the emissions performance of gasoline engines under transient operation .