Depending on the excitability of the medium, a propagating wave segment will either contract or expand to fill the medium with spiral waves. This paper aims to introduce a simple mechanism of feedback control to stabi...Depending on the excitability of the medium, a propagating wave segment will either contract or expand to fill the medium with spiral waves. This paper aims to introduce a simple mechanism of feedback control to stabilize such an expansion or contraction. To do this, we lay out a feedback control system in a block diagram and reduce it into a bare, universal formula. Analytical and experimental findings are compared through a series of numerical simulations of the Barkley model.展开更多
为提升随机路面与局部脉冲激励路面下的悬架平顺性,提出语义分割路面识别的主动悬架显式模型预测控制(Explicit Model Predict Control,EMPC)方法。建立2自由度主动悬架动力学模型;搭建基于空洞空间金字塔池化的DeepLabV3语义分割路面...为提升随机路面与局部脉冲激励路面下的悬架平顺性,提出语义分割路面识别的主动悬架显式模型预测控制(Explicit Model Predict Control,EMPC)方法。建立2自由度主动悬架动力学模型;搭建基于空洞空间金字塔池化的DeepLabV3语义分割路面识别网络,对网络进行训练及验证;设计基于路面识别的主动悬架EMPC控制策略,将悬架动力学模型转化为预测模型,确定代价函数和约束条件,根据路面识别结果匹配代价函数最优加权权重;离线划分系统状态参数区域,求解各状态分区内系统的最优控制律;在随机路面和脉冲路面下,将所设计的控制策略与被动悬架、线性二次高斯控制(Linear-quadratic-gaussian Control,LQG)进行仿真分析对比。相较于LQG控制,基于路面识别的主动悬架EMPC控制策略可在随机路面下改善悬架性能,且在脉冲路面下对悬架的调节时间降低20%以上,悬架的平顺性得到有效提升。展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11105074 and 11005026)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (Grant Nos. 11KJB140004 and 11KJA110001)the Qing Lan Project of Jiangsu Province, China
文摘Depending on the excitability of the medium, a propagating wave segment will either contract or expand to fill the medium with spiral waves. This paper aims to introduce a simple mechanism of feedback control to stabilize such an expansion or contraction. To do this, we lay out a feedback control system in a block diagram and reduce it into a bare, universal formula. Analytical and experimental findings are compared through a series of numerical simulations of the Barkley model.
文摘为提升随机路面与局部脉冲激励路面下的悬架平顺性,提出语义分割路面识别的主动悬架显式模型预测控制(Explicit Model Predict Control,EMPC)方法。建立2自由度主动悬架动力学模型;搭建基于空洞空间金字塔池化的DeepLabV3语义分割路面识别网络,对网络进行训练及验证;设计基于路面识别的主动悬架EMPC控制策略,将悬架动力学模型转化为预测模型,确定代价函数和约束条件,根据路面识别结果匹配代价函数最优加权权重;离线划分系统状态参数区域,求解各状态分区内系统的最优控制律;在随机路面和脉冲路面下,将所设计的控制策略与被动悬架、线性二次高斯控制(Linear-quadratic-gaussian Control,LQG)进行仿真分析对比。相较于LQG控制,基于路面识别的主动悬架EMPC控制策略可在随机路面下改善悬架性能,且在脉冲路面下对悬架的调节时间降低20%以上,悬架的平顺性得到有效提升。
