This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temp...This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.展开更多
当前自适应滤波前馈控制方法中具有代表性的是滤波-X最小均方(filtered-X least mean square,简称FXLMS)算法,它通常假定干扰源可测且作为前馈控制器的参考输入,但实际振动控制过程中需要考虑控制输出反馈信号对参考信号的影响,因此滤...当前自适应滤波前馈控制方法中具有代表性的是滤波-X最小均方(filtered-X least mean square,简称FXLMS)算法,它通常假定干扰源可测且作为前馈控制器的参考输入,但实际振动控制过程中需要考虑控制输出反馈信号对参考信号的影响,因此滤波-X算法面向实际应用具有较大的局限性。针对这一问题,以机敏压电太阳能帆板结构为模拟试验对象,提出一种基于IIR(infinite impulse response,简称IIR)结构的滤波-U最小均方(filtered-U least mean square,简称FULMS)自适应滤波控制方法,着重分析了控制器结构设计、FULMS算法推理过程、试验模型结构设计、试验平台的构建及其试验验证等环节。经过与FXLMS算法对比仿真试验,笔者所设计的控制算法控制效果良好。将其进行试验验证分析,结果表明,所采用的控制器设计方法与控制算法收敛速度快,控制效果好,为自适应振动控制方法向实际工程应用提供了较好的研究基础。展开更多
文摘This article presents an adaptive intelligent control strategy applied to a lumped-parameter evaporator model,i.e.,a simplified dynamic representation treating the evaporator as a single thermal node with uniform temperature distribution,suitable for control design due to its balance between physical fidelity and computational simplicity.The controller uses a wavelet-based neural proportional,integral,derivative(PID)controller with IIR filtering(infinite impulse response).The dynamic model captures the essential heat and mass transfer phenomena through a nonlinear energy balance,where the cooling capacity“Qevap”is expressed as a non-linear function of the compressor frequency and the temperature difference,specifically,Q_(evap)=k_(1)u(T_(in)−T_(e))with u as compressor frequency,Te evaporator temperature,and Tin inlet fluid temperature.The operating conditions of the system,in general terms,focus on the following variables,the overall thermal capacity is 1000 J/K,typical for small-capacity heat exchangers,The mass flow is 0.05 kg/s,typical for secondary liquid cooling circuits,the overall loss coefficient of 50 W/K that corresponds to small evaporators with partial insulation,the temperatures(inlet)of 10℃and the temperature of environment of 25℃,thermal load of 200 W that corresponds to a small-scaled air conditioning applications.To handle system nonlinearities and improve control performance,aMorlet wavelet-based neural network(Wavenet)is used to dynamically adjust the PID gains online.An IIR filter is incorporated to smooth the adaptive gains,improving stability and reducing oscillations.In contrast to prior wavelet-or neural-adaptive PID controllers in HVAC applications,which typically adjust gains without explicit filtering or not tailored to evaporator dynamics,this work introduces the first PID–Wavenet scheme augmented with an IIR-based stabilization layer,specifically designed to address the combined challenges of nonlinear evaporator behavior,gain oscillation,and real-time implementability.The proposed controller(PID-Wavenet+IIR)is implemented and validated inMATLAB/Simulink,demonstrating superior performance compared to a conventional PID tuned using Simulink’s auto-tuning function.Key results include a reduction in settling time from 13.3 to 8.2 s,a reduction in overshoot from 3.5%to 0.8%,a reduction in steady-state error from 0.12℃ to 0.02℃and a 13%reduction in energy overall consumption.The controller also exhibits greater robustness and adaptability under varying thermal loads.This explicit integration of wavelet-driven adaptation with IIR-filtered gain shaping constitutes the main methodological contribution and novelty of the work.These findings validate the effectiveness of the wavelet-based adaptive approach for advanced thermal management in refrigeration and HVAC systems,with potential applications in controlling variable-speed compressors,liquid chillers,and compact cooling units.
文摘当前自适应滤波前馈控制方法中具有代表性的是滤波-X最小均方(filtered-X least mean square,简称FXLMS)算法,它通常假定干扰源可测且作为前馈控制器的参考输入,但实际振动控制过程中需要考虑控制输出反馈信号对参考信号的影响,因此滤波-X算法面向实际应用具有较大的局限性。针对这一问题,以机敏压电太阳能帆板结构为模拟试验对象,提出一种基于IIR(infinite impulse response,简称IIR)结构的滤波-U最小均方(filtered-U least mean square,简称FULMS)自适应滤波控制方法,着重分析了控制器结构设计、FULMS算法推理过程、试验模型结构设计、试验平台的构建及其试验验证等环节。经过与FXLMS算法对比仿真试验,笔者所设计的控制算法控制效果良好。将其进行试验验证分析,结果表明,所采用的控制器设计方法与控制算法收敛速度快,控制效果好,为自适应振动控制方法向实际工程应用提供了较好的研究基础。
