Harmonic control of a dual-valve hydraulic servo system with dynamically allocated flows

Modern industries face increasing demands to improve the precision and workability of equipment, leading to stringent requirements for hydraulic servo systems in terms of flow, accuracy, and output power. High-flow servo valves and multi-valve synchronous control methods have seen more applications in the industry to meet these demands. However, high-flow servo valves are expensive and have unsatisfactory performance, and synchronous control methods require servo valves to have the same hydraulic characteristics, resulting in higher cost and lower accuracy of high-flow hydraulic servo systems. Also, very little has been conducted on controlling single actuators using multiple valves. In this study, we design a novel dual-valve hydraulic servo system structure with a high-flow proportional directional valve that is connected in parallel with a low-flow servo valve and further develop the mathematical model of this dual-valve system. Next, a harmonic control scheme was proposed, which included a flow allocation layer and trajectory tracking layer. Thereby, we achieved optimal control of two valves, and robust performance was guaranteed. Finally, we investigate the relationships between the flows of two valves, the deadband of the proportional directional valve, and trajectory tracking errors. Experimental results show that the proposed control scheme can effectively adjust the output flow of each valve dynamically according to the tracking trajectory and the hydraulic characteristics of the two valves, and the tracking performance of the servo system is significantly improved. This method is promised to enable the realization of an economical, high-flow, high-precision hydraulic servo system that can be generally used in various fields, such as marine engineering and construction.     

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.     

面向配电网弹性提升的需求响应优化配置策略

社会和经济的发展对电力系统供电质量提出更高的要求,尤其是与用户相连的配电网易受扰动影响,需加强其供能弹性。随着智能电网技术的发展,通过优化资源配置、灵活调度需求响应资源,可以提高电网供能弹性。采用基于力学映射模型的多阶段弹性评估模型量化需求响应的优化配置对配电网弹性的提升效果,并通过遗传算法求解该非线性优化问题。最后,以IEEE 33节点系统为例验证该模型在需求响应配置问题中的合理性和有效性,并分析优化配置方案对配电网弹性的提升效果。     

基于旋转门算法的电池储能辅助单机跟踪AGC指令控制策略

针对电池储能辅助单机跟踪AGC指令过程中机组爬坡频次高以及储能动作次数多的问题,提出基于旋转门算法的电池储能辅助单机跟踪AGC指令控制策略。首先,利用旋转门算法对AGC历史数据进行压缩,对压缩结果采用线性插值处理,进一步进行动态更新及实时修正,并结合机组爬坡特性确定机组承担的功率调节指令。然后,将储能系统动态地分为3个电池组,根据储能系统功率调节指令确定电池组动作顺序,进而设计功率分配方法实现储能系统上层功率分配,同时确定各电池组电池单元功率分配原则完成下层功率分配。最后,利用某电厂实际AGC指令对控制策略进行仿真分析,验证了所提策略的有效性。     

基于高斯混合模型的海洋能发电资源优化配置研究

针对海洋能资源丰富的海岛电网,提出了以消纳率最优为目标的海洋能发电资源优化配置方法。首先,采用高斯混合模型描述海洋能发电的随机波动性,并生成出力场景;然后以海洋能消纳率最优为目标构建了海岛电网的海洋能发电资源优化配置模型;最后,对渗透率、消纳率及源荷匹配度三个指标进行综合分析,获得最优配置方案。基于实际海岛负荷、波浪和潮流数据构建了海洋能发电资源优化配置算例,并重点分析了资源优化配置过程中渗透率约束的取值范围。结果表明,在不同的渗透率约束下,源荷匹配度呈现先升后降的趋势。因此,可以根据最大的源荷匹配度,确定资源优化配置模型中的渗透率约束取值范围。此外,在最优渗透率约束取值范围内,高斯混合模型出力场景与实测数据场景的优化配置结果相差不大且趋势一致,验证了高斯混合模型生成海洋能出力场景的有效性。     

认知雷达抗干扰中的博弈论分析综述

雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈,其作为电子战研究领域的热点一直备受学者们关注.该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略,主要通过不同体制的雷达利用认知技术感知和学习外界复杂的电磁环境,合理地分配发射功率、控制编码序列、设计波形、研究检测和跟踪方法以及分配雷达通信资源等.这样雷达既节约发射所消耗的功率,又可以自适应地搜索和跟踪目标而不被敌方所发现,从而使雷达在复杂多变的现代战场环境中达到自身最优的性能.最后,对认知雷达抗干扰中的博弈论分析研究进行总结和展望,并指出了一些博弈论在认知雷达抗干扰策略应用中所面临的潜在问题和挑战.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.     

基于HAGA的D2D-NOMA资源分配优化算法

针对无线系统带宽资源有限、基站负载压力大、传输时延长等问题,提出一种基于非正交多址接入技术的D2D系统吞吐量最大化资源分配算法。在不同用户的服务质量约束条件下,建立D2D系统吞吐量最大化资源分配模型。该模型的优化目标是一个混合整数非线性规划问题,将其解耦为信道匹配与功率分配2个子问题并分别进行处理,利用自适应惩罚函数法处理约束条件并提出一种基于爬山策略的自适应遗传算法以对问题进行求解。仿真结果表明,与GA、AGA算法相比,该算法能够有效提高D2D系统的吞吐量,且收敛性能更好。     

Due-window assignment and scheduling with general position-dependent processing times involving a deteriorating and compressible maintenance activity

In this paper, we consider common due-window assignment and scheduling problems with general position-dependent processing times involving deteriorating and compressible maintenance activity on a single machine. Two models associated with maintenance activity are examined in this article, in which the maintenance length is assumed to be either time-dependent and compressible or position-dependent and compressible. The objective is to find jointly the location and size of due-window, position of maintenance as well as resource amount allocated to it, and job sequence to minimise a total cost function based on earliness, tardiness, window location, window size and resource cost. We show that the problem considered in each of the two models’ setting can be optimally solved with polynomial time algorithm by reducing to assignment problem. Finally, two examples are provided to illustrate the solution procedures.