In this paper,direct adaptive-state feedback control schemes are developed to solve the problem of asymptotic tracking and disturbance rejection for a class of distributed large-scale systems with faulty and perturbed...In this paper,direct adaptive-state feedback control schemes are developed to solve the problem of asymptotic tracking and disturbance rejection for a class of distributed large-scale systems with faulty and perturbed interconnection links.In terms of the special distributed architectures,the adaptation laws are proposed to update controller parameters on-line when all interconnected fault factors,the upper bounds of perturbations in interconnection links,and external disturbances on subsystems axe unknown.Then,a class of distributed state feedback controllers is constructed to automatically compensate the fault and perturbation effects,and reject the disturbances simultaneously based on the information from adaptive schemes.The proposed adaptive robust tracking controllers can guarantee that the resulting adaptive closed-loop distributed system is stable and each subsystem can asymptotic-output track the corresponding reference signal in the presence of faults and perturbations in interconnection links,and external disturbances.The proposed design technique is finally evaluated in the light of a simulation example.展开更多
针对配电网可观测率低、可调节设备特性多样导致的电压调控难题,提出一种面向局部可观测场景的多时间尺度电压调控方法。首先,构建部分可观测的电压调控马尔可夫决策模型,并在模型中引入拓扑变化场景,以描述局部可观测情况下配电网运行...针对配电网可观测率低、可调节设备特性多样导致的电压调控难题,提出一种面向局部可观测场景的多时间尺度电压调控方法。首先,构建部分可观测的电压调控马尔可夫决策模型,并在模型中引入拓扑变化场景,以描述局部可观测情况下配电网运行状态与控制动作之间的动态关系。然后,采用双深度Q网络(double deep Q-network,DDQN)和孪生延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)算法分别训练离散和连续调节设备的电压调控智能体,实现多时间尺度电压调节。通过在训练过程中引入多样拓扑数据,使智能体能在变拓扑条件下学习调控策略,以提升其控制鲁棒性;同时,为解决引入多样拓扑场景造成的训练不均衡问题,提出基于拓扑重要度的采样策略以提升智能体训练收敛性。算例对比与分析验证了该方法在变拓扑条件下电压调控的有效性和优越性。展开更多
基金Supported by National Basic Research Program of China(973 Program)(2009CB320604)the Key Program of National Natural Science Foundation of China(60534010)+4 种基金National Natural Science Foundation of China(60674021),Program for New Century Excellent Talents in Universities(NCET-04-0283)the Funds for Cre-ative Research Groups of China(60821063)Program for Changjiang Scholars and Innovative Research Team in University(IRT0421)the Funds of Doctoral Program of Ministry of Education,China(20060145019)the 111 Project(B08015)
文摘In this paper,direct adaptive-state feedback control schemes are developed to solve the problem of asymptotic tracking and disturbance rejection for a class of distributed large-scale systems with faulty and perturbed interconnection links.In terms of the special distributed architectures,the adaptation laws are proposed to update controller parameters on-line when all interconnected fault factors,the upper bounds of perturbations in interconnection links,and external disturbances on subsystems axe unknown.Then,a class of distributed state feedback controllers is constructed to automatically compensate the fault and perturbation effects,and reject the disturbances simultaneously based on the information from adaptive schemes.The proposed adaptive robust tracking controllers can guarantee that the resulting adaptive closed-loop distributed system is stable and each subsystem can asymptotic-output track the corresponding reference signal in the presence of faults and perturbations in interconnection links,and external disturbances.The proposed design technique is finally evaluated in the light of a simulation example.
文摘针对配电网可观测率低、可调节设备特性多样导致的电压调控难题,提出一种面向局部可观测场景的多时间尺度电压调控方法。首先,构建部分可观测的电压调控马尔可夫决策模型,并在模型中引入拓扑变化场景,以描述局部可观测情况下配电网运行状态与控制动作之间的动态关系。然后,采用双深度Q网络(double deep Q-network,DDQN)和孪生延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)算法分别训练离散和连续调节设备的电压调控智能体,实现多时间尺度电压调节。通过在训练过程中引入多样拓扑数据,使智能体能在变拓扑条件下学习调控策略,以提升其控制鲁棒性;同时,为解决引入多样拓扑场景造成的训练不均衡问题,提出基于拓扑重要度的采样策略以提升智能体训练收敛性。算例对比与分析验证了该方法在变拓扑条件下电压调控的有效性和优越性。
