两层网络学习控制系统的快速优化调度策略、分布式计算及扩展应用
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摘要
飞速发展的网络和通信技术，与传统控制相互融合，应运而生了适合新世纪需求的网络控制系统。网络控制系统具有设计灵活、资源共享和实用性强等诸多优点，已经逐步应用于先进自动化制造、电力、航天和机器人等领域。然而，由通信网络所支撑的网络控制系统，受到网络容量和通信参量的约束，系统性能不仅取决于控制算法，很大程度上还取决于网络通信资源的合理有效调度。此外，随着控制需求的不断深化，控制算法和优化调度方法也趋于复杂，对有限的计算资源提出了严峻的挑战，系统无法满足算法的快速计算要求，影响整体性能。于是，本文在网络通信和计算资源有限的条件下，针对系统实时性的目标要求，提出基于快速计算平台的两层网络学习控制系统架构，研究公平优化调度策略，设计快速计算平台。主要工作概括如下：
首先，提出了基于快速计算平台的控制系统架构，分析了与传统网络控制系统相比所具有的不同特点，并在通信约束下，建立了具有公平性的多目标带宽优化调度模型。针对用户需求提高造成算法复杂的发展趋势和网络控制系统的实时性要求，在传统网络学习控制系统基础上引入快速计算平台，不但可以保证底层网络本地控制的高可靠性要求，也为计算资源的共享、控制优化的计算实时性提供架构保障。在此基础上，考虑通信约束下两层网络学习控制系统带宽调度问题，提出了具有公平性的非合作博弈模型，同时结合专家知识，设计了适合此控制系统架构的带宽调度效用函数，形成了兼具控制、网络多约束的多目标带宽调度优化策略方案。
其次，针对上述最优网络带宽调度问题的求解，提出了一种新的基于量子演化的优化方法。与传统的智能优化方法不同，基于量子演化的优化方法通过引入研究微观粒子的量子力学理论，使用量子态概率幅对搜索群的当前位置进行编码，由此增加解空间的遍历性；采用量子旋转门实现对群位置的变异和最优位置的搜索，这样可以进一步增加搜索种群的多样性，以避免优化算法早熟收敛，得到网络带宽调度问题的全局最优解；同时，通过适当设置量子旋转门的转角步长函数，可使算法实现任意精度的搜索。
第三，针对最优网络带宽调度的快速求解，提出了两种新的优化算法解决方案带有加速因子的混合蛙跳算法和分层阶级市场竞争算法。由于带有量子演化的优化方法虽能很好的提升最优解的质量，但其计算时间不稳定，仅适合两层网络学习控制系统带宽调度的离线优化。因此，提出了一种新的基于模因演化的加速混合蛙跳算法，模因演化用于进行个体和全局之间的信息交流，该算法集遗传模因算法和基于全局行为的粒子群算法的优点于一身，在保证解的质量的基础上，实现快速收敛。但是带有加速因子的混合蛙跳算法对重约束、尤其是大规模电力系统应用中的维度困境无能为力。受到微观经济学市场理论启发，由此提出了一种新的分层阶级市场竞争算法，通过分层的市场竞争过程，有效降低优化问题的维度和复杂度，同时又能保证解的质量和计算时间，适合快速求解大规模、重约束优化问题。
第四，为了进一步提高两层网络学习控制系统中复杂算法的计算速度，在本地计算资源有限的条件下，提出了基于合作博弈的网格并行计算与负载均衡策略。将网格计算融入两层网络学习控制系统，建立了计算任务并行化的计算模型，包含自私网格数学模型和外来作业计算成本模型，然后利用合作博弈理论进行模型的公平性分析，验证了负载均衡为全局最优策略；同时，提出了一种新的基于合作博弈的有界迭代负载均衡算法，给出了多集群自私网格负载的均衡方案，解决异构负载集群在自私网格中的负载均衡难题；最后，利用网格技术将分布式计算资源整合为高性能计算环境，通过计算任务的有效调度，来满足系统的实时计算要求。仿真结果验证了所提方案的有效性。
第五，提出了一种新的基于弹性计算云的两层网络学习控制系统的快速虚拟化计算方案。由于自私网格仍需要消耗一定的本地计算资源，同时整合分布式计算资源对整体系统的要求高，带来了居高不下的计算成本。因此，本文根据两层网络学习控制系统可变的计算需求，提出了一种高性能的具有虚拟化特征的集群架构，该架构支持设计灵活、成本低廉、通用性和可扩展性强的云计算，能动态地提供异构计算环境和集群负载均衡，并且能够避免对实际复杂分布式物理架构的讨论；其次，提出了一种新的弹性集群云资源性能评价模型，该模型能有效的计划分配集群来满足计算性能需求和成本要求；最后，设计了虚拟化外部弹性计算云平台，灵活地管理计算资源，同时满足可靠性和实时性要求，高效地完成两层网络学习控制系统中的动态计算任务。
最后，在上述理论研究和仿真结果验证所提方案有效性的基础上，将各项性能最优的分层阶级市场竞争算法结合专家系统的方法来解决电力系统中的难题
机组组合调度问题。首先分析了电力系统机组组合调度的优化需求，确定总成本目标函数和优化约束，结合多条专家规则组成专家系统来处理机组组合调度问题的多个复杂约束，并通过决策发电机组的初始运行状态的预调度处理，保证所有搜索区域为靠近最优解的可行解集，提高搜索效率，减少执行时间。通过在虚拟化弹性计算云仿真平台上对10台至100台机组的电力系统调度进行实验，表明提出的分层阶级市场竞争算法结合专家系统的新方法能更快更优的解决机组组合调度问题，不仅大大节省了计算时间，对机组组合调度成本的减少也十分有效，具有良好的应用前景。
With the rapid development of network and communication technology,intergrating with the traditional control theory, networked control systems (NCS)emerge as the times require. Networked control system has been gradually applied tothe field of advanced automatic manufacturing, electric power, aerospace and robot,due to various advantages including flexible design, resources sharing, andpracticability. However, network control system, which is supported by commoncommunication network, is subject to many constraints of network capacity andcommunication parameters. To some extent, the performance of the system not onlydepends on control algorithms, but on reasonably effective scheduling of networkcommunication resources as well. In the meanwhile, control algorithms andoptimization scheduling methods trend to be more complex in order to meet thedeepening needs of control. Also, it is really a severe challenge to meet the fastcomputational requirements with limited computing resources, which can further affectthe overall system performance. Therefore, under the limited network communicationbandwidth and computing resources, a two-layer networked learning control system(NLCS) architecture based on fast computing platform is proposed to reach the goal ofsystem real-time requirements. The focus of this paper is on the development of fairoptimization scheduling strategy and the design of fast computing platform. The mainwork is summarized as follows:
Firstly, a two-layer NLCS architecture based on fast computing platform ispresented and the differences with NCS are analyzed. A fair multi-objective bandwidthoptimization scheduling model with communication constraints is formulated. For thecomplexity of the algorithms caused by increasing demand of users and the real-timerequirements in networked control system, we introduced a fast computing platforminto the traditional networked learning control system. This new architecture not onlycan guarantee the high reliability of the local control network, but also ensure real-timerequirement of computing resources sharing and calculations control and optimization.Furthermore, considering the bandwidth scheduling problem with communicationconstraints in a two-layer networked learning control system, a fair non-cooperativegame model combined with expert knowledge is proposed. A suitable utility functionis also designed, forming multi-constrained network bandwidth scheduling optimization strategy.
Secondly, for solving the optimal network bandwidth scheduling problem, a newoptimization method based on quantum evolution is proposed. Unlike traditionalintelligent optimization methods, by introducing microscopic particles in the theory ofquantum mechanics, quantum state probability amplitude is used in quantum-inspiredoptimization method to encode the current position of the search group for increasingthe diversification of the solution space. Moreover, quantum rotation gates are utilizedfor the variability of group position and the search of the optimal location, which canfurther increase the diversity of search population. The quantum-inspired optimizationmethod can gain the convergence to a global optimal solution to the networkbandwidth scheduling problem, avoiding sticking into local optimal solution.Meanwhile, through setting of proper value for step function of quantum gate, we canachieve algorithm search with arbitrary precision.
Thirdly, two new optimization algorithm solutions are proposed for fast solvingthe optimal network bandwidth scheduling problem accelerating factor basedshuffled frog leaping algorithm (SFLA) and two-layer hierarchical market competitionalgorithm (THMCA). Although quantum-inspired optimization method is able toenhance the quality of the optimal solution, its calculation time is instability. Thismethod is only suitable for offline optimization. Consequently, a new meta-heuristicoptimization method with accelerating factor called SFLA is proposed, which isefficient in finding global solutions. The SFLA combines the advantages of the boththe genetic-based memetic algorithm (MA) and the social behavior-based PSOalgorithm. The most distinguished advantage of SFLA is its fast convergence speed.However SFLA suffers from the curse of dimensionality problem, especially in solvingheavy constrains, large scale optimization problem in power system. Inspired bycompetitions among enterprises in economic activities, a novel two-layer hierarchicalmarket competition algorithm (THMCA) is proposed. Market competitions amongthese conglomerates lead to the convergence to a monopoly at the end, resulting in anoptimal solution of the above problem. The algorithm can effectively reduce thedimensions and complexity of the optimization problem, ensuring the quality ofsolutions and computing time, which is suitable for seeking fast solution of large-scaleconstrained optimization problem.
Fourthly, in order to further improve the calculation speed of complex algorithmsin two-layer NLCS with limited local computing resources, cooperative game based parallel grid computing and load balancing strategy are established. Integrating gridcomputing into the two-layer NLCS, the computational model of parallel computingtasks is formulated, including selfish grid mathematical models and foreign job costmodel. And cooperative game theory based fair analysis of the model verify that loadbalancing is the global optimal strategy. Furthermore, a novel load balancing scheme,cooperative game based bounded iterative load balancing algorithm, is proposed forheterogeneous load clusters in the multi-cluster selfish grid. Finally, distributedcomputing resources are intergrated as high-performance computing platform.Effective scheduling of computing tasks is used to meet the real-time computingrequirements.
Fifthly, we investigate a flexible elastic cloud computing based fast virtualdeployment scheme for a two-layer NLCS. Selfish grid still needs to consume a certainamount of local computing resources. And integrating distributed computing resourceshave high computing costs of the overall system. Therefore, high performance clusterarchitecture with virtual properties that hide the complexity of distributed physicalinfrastructures is presented. The architecture is support for cloud computing todynamically deliver heterogeneous computational environments and partition thecluster capacity, adapting to variable demands in a networked control system. Also, aperformance model employing cloud resources for elastic clusters is developed, whichplans the capacity of the cluster to meet a performance policy and cost request tocomplete a given work load. The performance of model has been evaluated in theexecution of heuristic computing workloads. Finally, the comparison experimentalresults have demonstrated that the virtualization based elastic clusters constitute afeasible and high performing computing platform for a two-layer NLCS.
Finally, after the theoretical research and simulation results gain the effectiveverification of the proposed scheme, the best two-layer hierarchical marketcompetition algorithm (THMCA) combined with expert system is applied for solvingthe unit commitment problem in power systems under the fast elastic cloud computingplatform. Expert system are used to produce several expert rules for heavy constraintshandling not only in the prescheduling process and in the THMCA process as well,ensuring that the positions of all companies are feasible and near-optimal solutions tothe UC problem. The algorithm running on fast elastic cloud computing platform isshown to have a fast execution speed for UC application and the comparisonsimulation results on a power system with up to100generating units have demonstrated the effectiveness on cost reduction of the proposed method.
首先，提出了基于快速计算平台的控制系统架构，分析了与传统网络控制系统相比所具有的不同特点，并在通信约束下，建立了具有公平性的多目标带宽优化调度模型。针对用户需求提高造成算法复杂的发展趋势和网络控制系统的实时性要求，在传统网络学习控制系统基础上引入快速计算平台，不但可以保证底层网络本地控制的高可靠性要求，也为计算资源的共享、控制优化的计算实时性提供架构保障。在此基础上，考虑通信约束下两层网络学习控制系统带宽调度问题，提出了具有公平性的非合作博弈模型，同时结合专家知识，设计了适合此控制系统架构的带宽调度效用函数，形成了兼具控制、网络多约束的多目标带宽调度优化策略方案。
其次，针对上述最优网络带宽调度问题的求解，提出了一种新的基于量子演化的优化方法。与传统的智能优化方法不同，基于量子演化的优化方法通过引入研究微观粒子的量子力学理论，使用量子态概率幅对搜索群的当前位置进行编码，由此增加解空间的遍历性；采用量子旋转门实现对群位置的变异和最优位置的搜索，这样可以进一步增加搜索种群的多样性，以避免优化算法早熟收敛，得到网络带宽调度问题的全局最优解；同时，通过适当设置量子旋转门的转角步长函数，可使算法实现任意精度的搜索。
第三，针对最优网络带宽调度的快速求解，提出了两种新的优化算法解决方案带有加速因子的混合蛙跳算法和分层阶级市场竞争算法。由于带有量子演化的优化方法虽能很好的提升最优解的质量，但其计算时间不稳定，仅适合两层网络学习控制系统带宽调度的离线优化。因此，提出了一种新的基于模因演化的加速混合蛙跳算法，模因演化用于进行个体和全局之间的信息交流，该算法集遗传模因算法和基于全局行为的粒子群算法的优点于一身，在保证解的质量的基础上，实现快速收敛。但是带有加速因子的混合蛙跳算法对重约束、尤其是大规模电力系统应用中的维度困境无能为力。受到微观经济学市场理论启发，由此提出了一种新的分层阶级市场竞争算法，通过分层的市场竞争过程，有效降低优化问题的维度和复杂度，同时又能保证解的质量和计算时间，适合快速求解大规模、重约束优化问题。
第四，为了进一步提高两层网络学习控制系统中复杂算法的计算速度，在本地计算资源有限的条件下，提出了基于合作博弈的网格并行计算与负载均衡策略。将网格计算融入两层网络学习控制系统，建立了计算任务并行化的计算模型，包含自私网格数学模型和外来作业计算成本模型，然后利用合作博弈理论进行模型的公平性分析，验证了负载均衡为全局最优策略；同时，提出了一种新的基于合作博弈的有界迭代负载均衡算法，给出了多集群自私网格负载的均衡方案，解决异构负载集群在自私网格中的负载均衡难题；最后，利用网格技术将分布式计算资源整合为高性能计算环境，通过计算任务的有效调度，来满足系统的实时计算要求。仿真结果验证了所提方案的有效性。
第五，提出了一种新的基于弹性计算云的两层网络学习控制系统的快速虚拟化计算方案。由于自私网格仍需要消耗一定的本地计算资源，同时整合分布式计算资源对整体系统的要求高，带来了居高不下的计算成本。因此，本文根据两层网络学习控制系统可变的计算需求，提出了一种高性能的具有虚拟化特征的集群架构，该架构支持设计灵活、成本低廉、通用性和可扩展性强的云计算，能动态地提供异构计算环境和集群负载均衡，并且能够避免对实际复杂分布式物理架构的讨论；其次，提出了一种新的弹性集群云资源性能评价模型，该模型能有效的计划分配集群来满足计算性能需求和成本要求；最后，设计了虚拟化外部弹性计算云平台，灵活地管理计算资源，同时满足可靠性和实时性要求，高效地完成两层网络学习控制系统中的动态计算任务。
最后，在上述理论研究和仿真结果验证所提方案有效性的基础上，将各项性能最优的分层阶级市场竞争算法结合专家系统的方法来解决电力系统中的难题
机组组合调度问题。首先分析了电力系统机组组合调度的优化需求，确定总成本目标函数和优化约束，结合多条专家规则组成专家系统来处理机组组合调度问题的多个复杂约束，并通过决策发电机组的初始运行状态的预调度处理，保证所有搜索区域为靠近最优解的可行解集，提高搜索效率，减少执行时间。通过在虚拟化弹性计算云仿真平台上对10台至100台机组的电力系统调度进行实验，表明提出的分层阶级市场竞争算法结合专家系统的新方法能更快更优的解决机组组合调度问题，不仅大大节省了计算时间，对机组组合调度成本的减少也十分有效，具有良好的应用前景。
With the rapid development of network and communication technology,intergrating with the traditional control theory, networked control systems (NCS)emerge as the times require. Networked control system has been gradually applied tothe field of advanced automatic manufacturing, electric power, aerospace and robot,due to various advantages including flexible design, resources sharing, andpracticability. However, network control system, which is supported by commoncommunication network, is subject to many constraints of network capacity andcommunication parameters. To some extent, the performance of the system not onlydepends on control algorithms, but on reasonably effective scheduling of networkcommunication resources as well. In the meanwhile, control algorithms andoptimization scheduling methods trend to be more complex in order to meet thedeepening needs of control. Also, it is really a severe challenge to meet the fastcomputational requirements with limited computing resources, which can further affectthe overall system performance. Therefore, under the limited network communicationbandwidth and computing resources, a two-layer networked learning control system(NLCS) architecture based on fast computing platform is proposed to reach the goal ofsystem real-time requirements. The focus of this paper is on the development of fairoptimization scheduling strategy and the design of fast computing platform. The mainwork is summarized as follows:
Firstly, a two-layer NLCS architecture based on fast computing platform ispresented and the differences with NCS are analyzed. A fair multi-objective bandwidthoptimization scheduling model with communication constraints is formulated. For thecomplexity of the algorithms caused by increasing demand of users and the real-timerequirements in networked control system, we introduced a fast computing platforminto the traditional networked learning control system. This new architecture not onlycan guarantee the high reliability of the local control network, but also ensure real-timerequirement of computing resources sharing and calculations control and optimization.Furthermore, considering the bandwidth scheduling problem with communicationconstraints in a two-layer networked learning control system, a fair non-cooperativegame model combined with expert knowledge is proposed. A suitable utility functionis also designed, forming multi-constrained network bandwidth scheduling optimization strategy.
Secondly, for solving the optimal network bandwidth scheduling problem, a newoptimization method based on quantum evolution is proposed. Unlike traditionalintelligent optimization methods, by introducing microscopic particles in the theory ofquantum mechanics, quantum state probability amplitude is used in quantum-inspiredoptimization method to encode the current position of the search group for increasingthe diversification of the solution space. Moreover, quantum rotation gates are utilizedfor the variability of group position and the search of the optimal location, which canfurther increase the diversity of search population. The quantum-inspired optimizationmethod can gain the convergence to a global optimal solution to the networkbandwidth scheduling problem, avoiding sticking into local optimal solution.Meanwhile, through setting of proper value for step function of quantum gate, we canachieve algorithm search with arbitrary precision.
Thirdly, two new optimization algorithm solutions are proposed for fast solvingthe optimal network bandwidth scheduling problem accelerating factor basedshuffled frog leaping algorithm (SFLA) and two-layer hierarchical market competitionalgorithm (THMCA). Although quantum-inspired optimization method is able toenhance the quality of the optimal solution, its calculation time is instability. Thismethod is only suitable for offline optimization. Consequently, a new meta-heuristicoptimization method with accelerating factor called SFLA is proposed, which isefficient in finding global solutions. The SFLA combines the advantages of the boththe genetic-based memetic algorithm (MA) and the social behavior-based PSOalgorithm. The most distinguished advantage of SFLA is its fast convergence speed.However SFLA suffers from the curse of dimensionality problem, especially in solvingheavy constrains, large scale optimization problem in power system. Inspired bycompetitions among enterprises in economic activities, a novel two-layer hierarchicalmarket competition algorithm (THMCA) is proposed. Market competitions amongthese conglomerates lead to the convergence to a monopoly at the end, resulting in anoptimal solution of the above problem. The algorithm can effectively reduce thedimensions and complexity of the optimization problem, ensuring the quality ofsolutions and computing time, which is suitable for seeking fast solution of large-scaleconstrained optimization problem.
Fourthly, in order to further improve the calculation speed of complex algorithmsin two-layer NLCS with limited local computing resources, cooperative game based parallel grid computing and load balancing strategy are established. Integrating gridcomputing into the two-layer NLCS, the computational model of parallel computingtasks is formulated, including selfish grid mathematical models and foreign job costmodel. And cooperative game theory based fair analysis of the model verify that loadbalancing is the global optimal strategy. Furthermore, a novel load balancing scheme,cooperative game based bounded iterative load balancing algorithm, is proposed forheterogeneous load clusters in the multi-cluster selfish grid. Finally, distributedcomputing resources are intergrated as high-performance computing platform.Effective scheduling of computing tasks is used to meet the real-time computingrequirements.
Fifthly, we investigate a flexible elastic cloud computing based fast virtualdeployment scheme for a two-layer NLCS. Selfish grid still needs to consume a certainamount of local computing resources. And integrating distributed computing resourceshave high computing costs of the overall system. Therefore, high performance clusterarchitecture with virtual properties that hide the complexity of distributed physicalinfrastructures is presented. The architecture is support for cloud computing todynamically deliver heterogeneous computational environments and partition thecluster capacity, adapting to variable demands in a networked control system. Also, aperformance model employing cloud resources for elastic clusters is developed, whichplans the capacity of the cluster to meet a performance policy and cost request tocomplete a given work load. The performance of model has been evaluated in theexecution of heuristic computing workloads. Finally, the comparison experimentalresults have demonstrated that the virtualization based elastic clusters constitute afeasible and high performing computing platform for a two-layer NLCS.
Finally, after the theoretical research and simulation results gain the effectiveverification of the proposed scheme, the best two-layer hierarchical marketcompetition algorithm (THMCA) combined with expert system is applied for solvingthe unit commitment problem in power systems under the fast elastic cloud computingplatform. Expert system are used to produce several expert rules for heavy constraintshandling not only in the prescheduling process and in the THMCA process as well,ensuring that the positions of all companies are feasible and near-optimal solutions tothe UC problem. The algorithm running on fast elastic cloud computing platform isshown to have a fast execution speed for UC application and the comparisonsimulation results on a power system with up to100generating units have demonstrated the effectiveness on cost reduction of the proposed method.
引文
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