Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhan...Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhanced twoloop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints.The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint.Contrary to the MPC-based RGs,the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints.Additionally,the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation.Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.展开更多
为了实现城市污水处理过程各性能指标的优化运行,提出了一种动态分解多目标粒子群优化控制(optimal control based on dynamic decomposed multiobjective particle swarm optimization, OC-DDMOPSO)策略.首先,构建了基于自适应核函数...为了实现城市污水处理过程各性能指标的优化运行,提出了一种动态分解多目标粒子群优化控制(optimal control based on dynamic decomposed multiobjective particle swarm optimization, OC-DDMOPSO)策略.首先,构建了基于自适应核函数的运行性能指标模型,确定了优化运行目标.其次,设计了基于档案库动态分解的多目标粒子群优化算法,实时获取操作变量的优化设定值.最后,利用预测控制策略跟踪优化设定值,完成了城市污水处理过程优化控制.将提出的OC-DDMOPSO应用于基准仿真平台BSM1,实验结果显示,OC-DDMOPSO能够实现城市污水处理过程稳定运行,保证出水水质达标排放和降低运行成本.展开更多
In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use ...In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use of predictive arguments with a twofold aim:1)Promptly detect malicious agent behaviors affecting normal system operations;2)Apply specific control actions,based on predictive ideas,for mitigating as much as possible undesirable domino effects resulting from adversary operations.Specifically,the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent.Finally,numerical simulations are carried out to show benefits and effectiveness of the proposed approach.展开更多
The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can ...The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.展开更多
文摘Model predictive controllers(MPC)with the two-loop scheme are successful approaches practically and can be classified into two main categories,tube-based MPC and MPCbased reference governors(RG).In this paper,an enhanced twoloop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints.The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint.Contrary to the MPC-based RGs,the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints.Additionally,the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation.Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.
文摘为了实现城市污水处理过程各性能指标的优化运行,提出了一种动态分解多目标粒子群优化控制(optimal control based on dynamic decomposed multiobjective particle swarm optimization, OC-DDMOPSO)策略.首先,构建了基于自适应核函数的运行性能指标模型,确定了优化运行目标.其次,设计了基于档案库动态分解的多目标粒子群优化算法,实时获取操作变量的优化设定值.最后,利用预测控制策略跟踪优化设定值,完成了城市污水处理过程优化控制.将提出的OC-DDMOPSO应用于基准仿真平台BSM1,实验结果显示,OC-DDMOPSO能够实现城市污水处理过程稳定运行,保证出水水质达标排放和降低运行成本.
文摘In this paper,a resilient distributed control scheme against replay attacks for multi-agent networked systems subject to input and state constraints is proposed.The methodological starting point relies on a smart use of predictive arguments with a twofold aim:1)Promptly detect malicious agent behaviors affecting normal system operations;2)Apply specific control actions,based on predictive ideas,for mitigating as much as possible undesirable domino effects resulting from adversary operations.Specifically,the multi-agent system is topologically described by a leader-follower digraph characterized by a unique leader and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to instantaneously recognize the attacked agent.Finally,numerical simulations are carried out to show benefits and effectiveness of the proposed approach.
基金This study was supported by the National Natural Science Foundation of China(No.51778321):Research on the quantitative description and simulation methodology of occupant behavior in buildingsthe Innovative Research Groups of the National Natural Science Foundation of China(No.51521005)also the Tsinghua University tutor research fund.
文摘The lighting system accounts for 8%of the total electricity consumption in commercial buildings in the United States and 12%of the total electricity consumption in public buildings globally.This consumption share can be effectively reduced using the demand-response control.The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared(PIR)sensor.However,the detection inaccuracy of the PIR sensor usually results in false-offs.To diminish the false-error frequency,the existing lighting system control simply deploys a delayed reaction period(e.g.,5 to 20 min),which is not sufficiently accurate for the demand-response operation.Therefore,in this research,a novel data-driven model predictive control(MPC)method that is based on the temporal sequential-based artificial neural network(TS-ANN)is proposed to overcome this challenge using an updated historical occupancy status.Using an office as case study,the proposed model is also compared with the traditional lighting system control method.In the proposed model,the occupancy data was trained to predict the occupancy pattern to improve the control.It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature.The simulation results indicated that the proposed method achieved higher accuracy(97.4%)and fewer false-offs(from 79.5 with traditional time delay method to 0.6 times per day)are achieved by the MPC model.The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.
文摘针对直升机时间域瞬变电磁(helicopter transient electromagnetic,HTEM)发射系统对电流响应快速跟踪的技术需求,提出一种基于Boost拓扑电路的峰值电流数字预测控制算法.通过采样当前开关周期的输入电压、输出电压和电感电流,预测下一个开关周期结束时刻的电感电流,进而推导出占空比,使电感电流在一个开关响应周期实现对参考值的跟踪,提高电流的瞬态响应速度,并通过伯德图对闭环系统的稳定性和快速性进行分析.控制算法不仅简单,而且易于在数字信号处理器(digital signal processors,DSP)上实现.最后,通过软件仿真和实验对提出的控制算法进行了验证.