基于模糊逻辑的空调系统广义预测控制参数整定

GPC tuning for air-conditioning systems based on fuzzy logic

针对多输入多输出(multiple-input-multiple-output,MIMO)变风量空调系统(variable air volume,VAV),本文提出了一种基于模糊逻辑和事件触发的广义预测控制(generalized predictive control,GPC)参数整定方法。针对多变量VAV空调系统,基于输出斜率等新型模糊目标参数及高斯双边形隶属度函数建立模糊预测模型,提高了隶属函数拟合度以更贴切地反映系统当前时刻状态。利用麻雀智能算法(sparrow search algorithm,SSA)构建隶属度函数,对GPC控制器中的柔化因子和加权系数进行在线分段整定,有效提升了系统性能。此外,在参数整定过程中,引入事件触发机制(event-triggered mechanism,ETM),在保证控制性能的同时,避免了不必要的控制器采样与更新,降低了系统在线计算量并减少了能源消耗。最后通过VAV空调系统仿真实验验证,证明了本文提出方法的可行性和有效性。

For a multiple-input-multiple-output variable air volume(VAV)air-conditioning system,a generalized predictive control(GPC)parameter tuning method based on fuzzy logic and event triggering is proposed,establis-hing a fuzzy prediction model based on the output slope and new fuzzy target parameters and the Gaussian bilateral membership function to improve the membership function and better reflect the current state of the system.Subse-quently,the sparrow search algorithm(SSA)is used to construct the membership function to segmentally adjust the softening factor and weighting coefficient in the GPC controller online,which has effectively improved the system performance.Moreover,in the parameter tuning process,the event trigger mechanism is introduced to avoid unnec-essary controller sampling and update while ensuring control performance.It reduces the amount of system online calculations and energy consumption.Finally,the simulation experiment of the VAV air-conditioning system proves the feasibility and effectiveness of the proposed method.