This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merel...This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.展开更多
The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various faul...The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.展开更多
电-气综合能源系统(integrated energy system,IES)的发展有助于提高能源效率并支撑可持续能源转型。电力网络和天然气网络通常隶属于不同的运营主体,这制约了IES的能源利用效率和多能互济协同。在此背景下,提出一种各能源子系统独立优...电-气综合能源系统(integrated energy system,IES)的发展有助于提高能源效率并支撑可持续能源转型。电力网络和天然气网络通常隶属于不同的运营主体,这制约了IES的能源利用效率和多能互济协同。在此背景下,提出一种各能源子系统独立优化的分布式最优调度方法。建立了电力网络潮流约束、天然气网络管网约束、电-气耦合约束下的IES集中式控制模型,并利用凸松弛技术和大M法对非凸约束进行了转化;基于交替方向乘子法(alternating direction method of multipliers,ADMM)对集中式控制模型进行解耦,使其转化为电力网络和天然气网络独立优化的分布式协同控制模型,并给出了电-气IES分布式控制方法的实施流程;用算例系统对所提方法的可行性和有效性做了验证。展开更多
The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing systems.When the two problems are considered simultaneously,the resulting problem i...The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing systems.When the two problems are considered simultaneously,the resulting problem is much more complex than either of them.Obtaining sufficient training data for conventional data-based optimization approaches is difficult because of the high diversity of system structures.Consequently,optimization of multi-line systems with alternative machines requires a simple mechanism and must be minimally dependent on historical data.To define a general multi-line system with alternative machines,this study introduces the capability vector and matrix and the distribution vector and matrix.A naive optimization method is proposed in accordance with classic feedback control theory,and its key approaches are introduced.When a reasonable target value is provided,the proposed method can realize closed-loop optimization to the selected objective performance.Case studies are performed on a real 5/6-inch semiconductor wafer manufacturing facility and a simulated multi-line system constructed on the basis of the MiniFAB model.Results show that the proposed method can effectively and efficiently optimize various objective performance.The method demonstrates a potential for utilization in multi-objective optimization.展开更多
基金the National Natural Science Foundation of China(61833012,61773162,61590924)the Natural Science Foundation of Shanghai(18ZR1420000)。
文摘This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.
文摘The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.
文摘电-气综合能源系统(integrated energy system,IES)的发展有助于提高能源效率并支撑可持续能源转型。电力网络和天然气网络通常隶属于不同的运营主体,这制约了IES的能源利用效率和多能互济协同。在此背景下,提出一种各能源子系统独立优化的分布式最优调度方法。建立了电力网络潮流约束、天然气网络管网约束、电-气耦合约束下的IES集中式控制模型,并利用凸松弛技术和大M法对非凸约束进行了转化;基于交替方向乘子法(alternating direction method of multipliers,ADMM)对集中式控制模型进行解耦,使其转化为电力网络和天然气网络独立优化的分布式协同控制模型,并给出了电-气IES分布式控制方法的实施流程;用算例系统对所提方法的可行性和有效性做了验证。
基金This research was supported in part by the National Natural Science Foundation of China(Grant No.71690230/71690234)the International S&T Cooperation Program of China(Grant No.2017YFE0101400).
文摘The scheduling of parallel machines and the optimization of multi-line systems are two hotspots in the field of complex manufacturing systems.When the two problems are considered simultaneously,the resulting problem is much more complex than either of them.Obtaining sufficient training data for conventional data-based optimization approaches is difficult because of the high diversity of system structures.Consequently,optimization of multi-line systems with alternative machines requires a simple mechanism and must be minimally dependent on historical data.To define a general multi-line system with alternative machines,this study introduces the capability vector and matrix and the distribution vector and matrix.A naive optimization method is proposed in accordance with classic feedback control theory,and its key approaches are introduced.When a reasonable target value is provided,the proposed method can realize closed-loop optimization to the selected objective performance.Case studies are performed on a real 5/6-inch semiconductor wafer manufacturing facility and a simulated multi-line system constructed on the basis of the MiniFAB model.Results show that the proposed method can effectively and efficiently optimize various objective performance.The method demonstrates a potential for utilization in multi-objective optimization.
