Fixed-time distributed optimization for multi-agent systems with external disturbances over directed networks

This article considers the fixed-time distributed optimization problem of multi-agent systems with external disturbances, in which the global optimization objective is a convex combination of local objective functions. To solve this issue, a directed communication network is carefully designed, and an integral sliding mode control protocol is proposed based on the gradient of global objective function first. Moreover, two distributed optimal protocols are designed by using the gradient and the Hessian matrix of local objective function, respectively. By employing Lyapunov stability theory, graph theory, convex analysis, and inequality techniques, we prove that all proposed protocols can make agents achieve consensus and converge accurately to the optimal solution of the considered problem in some fixed-time intervals. Finally, some numerical simulations are given to verify the feasibility of the theoretical results.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

Demand response (DR) using shared energy storage systems (ESSs) is an appealing method to save electricity bills for users under demand charge and time-of-use (TOU) price. A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this study. In this scheme, the interactions between selfish users and an operator are characterized as a Stackelberg game. Operator holds a large-scale ESS that is shared among users in the form of energy transactions. It sells energy to users and sets the selling price first. It maximizes its profit through optimal pricing and ESS dispatching. Users purchase some energy from operator for the reduction of their demand charges after operator’s selling price is announced. This game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization model. The upper-level optimization maximizes operator’s profit and the lower-level optimization minimizes users’ costs. The bi-level model is transformed and linearized into a mixed-integer linear programming (MILP) model using the mathematical programming with equilibrium constraints (MPEC) method and model linearizing techniques. Case studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.      

无刷电机驱动器在大型医疗设备上的应用

无刷电机及其驱动器在大型医疗上的设备应用越来越广泛。为了设计更好的医疗设备、造福患者,研发人员需要对驱动器有更深的理解和更好的选型及应用。需要理解无刷电机及驱动器的工作原理,清楚大型医疗设备对无刷电机驱动器的要求和实现方式,如安全性、可靠性、通讯等。在医疗设备开发中,驱动器的应用有多种方案,它们各具特点,包括通用驱动器,板载驱动器、自主研发驱动器。随着技术的发展,无刷电机驱动器在大型医疗设备的应用,出现了新的趋势,如:小型化、一体化、模块化和集成化。     

服务差异二级选址路径问题及大邻域搜索算法

在电子商务背景下,物流公司为了实现及时交付并缓解交通拥堵,可按照客户不同的服务需求,将其分为自取型和配送型,进而选择开放自助点或配送点对客户进行服务。自助点和配送点作为二级物流设施,配送中心作为一级物流设施,由此形成了二级选址-路径问题。在配送车数量约束、自助点容量约束等限制下,兼顾客户点物品回收,以总物流成本最小为目标,建立考虑服务差异的二级选址-路径问题模型。设计自适应大邻域搜索算法进行求解,通过Nguyen的2E-LRP算例进行测试,更新了1个算例的全球最优解,其余算例达到或接近最优解,平均Gap值在1.22%以内,验证了算法的有效性及良好的收敛性。实际案例模拟分析验证了模型的有效性和适用性,可以为相关物流企业提供决策依据和参考。     

A post quantum secure construction of an authentication protocol for satellite communication

Satellite's communication system is used to communicate under significant distance and circumstances where the other communication systems are not comfortable. Since all the data are exchanged over a public channel, so the security of the data is an essential component for the communicating parties. Both key exchange and authentication are two cryptographic tools to establish a secure communication between two parties. Currently, various kinds of authentication protocols are available to establish a secure network, but all of them depend on number–theoretical (discrete logarithm problem/factorization assumption) hard assumptions. Due to Shor's and Grover's computing algorithm number theoretic assumptions are breakable by quantum computers. Although Kumar and Garg have proposed a quantum attack-resistant protocol for satellite communication, it cannot resist stolen smart card attack. We have analyzed that how Kumar and Garg is vulnerable to the stolen smart card attack using differential power analysis attack described in He et al and Chen and Chen. We have also analyzed the modified version of signal leakage attack and sometimes called improved signal leakage attack on Kumar and Garg's protocol. We have tried to construct a secure and efficient authentication protocol for satellites communication that is secure against quantum computing. This is more efficient as it requires only three messages of exchange. This paper includes security proof and performance of the proposed authentication and key agreement protocol.     

非零和微分博弈系统的事件触发最优跟踪控制（英文）

近年来,对于具有未知动态的非零和微分博弈系统的跟踪问题,已经得到了讨论,然而这些方法是时间触发的,在传输带宽和计算资源有限的环境下并不适用.针对具有未知动态的连续时间非线性非零和微分博弈系统,本文提出了一种基于积分强化学习的事件触发自适应动态规划方法.该策略受梯度下降法和经验重放技术的启发,利用历史和当前数据更新神经网络权值.该方法提高了神经网络权值的收敛速度,消除了一般文献设计中常用的初始容许控制假设.同时,该算法提出了一种易于在线检查的持续激励条件(通常称为PE),避免了传统的不容易检查的持续激励条件.基于李亚普诺夫理论,证明了跟踪误差和评价神经网络估计误差的一致最终有界性.最后,通过一个数值仿真实例验证了该方法的可行性.     

一种大属性域版本控制的云安全用户属性动态撤销策略

密文策略属性基加密(ciphertext-policy attribute-based encryption, CP-ABE) 作为一种一对多的数据加密技术, 因能实现密文数据安全和细粒度的权限访问控制而引起学术界的广泛关注。尽管目前在该领域已取得了一些研究成果, 然而, 大多数CP-ABE 方案均基于小属性域, 系统属性同时被多个用户共享而难以实现动态的属性撤销, 现有的属性撤销机制在功能复杂性、计算高效性、以及抗合谋攻击安全性方面存在的问题都成为它在实际应用中的障碍。针对上述问题, 提出一种大属性域版本控制的云安全用户属性动态撤销策略。该方案在密文策略属性加密中构造属性及用户版本密钥, 通过更新属性版本密钥实现用户属性撤销, 更新用户版本密钥实现用户撤销。由此避免了基于重加密实现撤销带来的计算和通信开销。该方案基于 q-DBPBDHE假设, 在随机预言模型下证明是静态性安全的。最后, 对方案进行了性能分析与实验验证, 实验结果表明:在保证密文前后向安全性的前提下, 该方案可以实现动态的用户属性撤销和用户撤销且可以抵制多重合谋攻击, 较同类方案本文方案具有较优的功能特性和计算效率。此外, 所提方案基于大属性域, 在实际应用中更加灵活。     

联合聚类方法和深度学习的混凝土坝变形预测

混凝土大坝变形预测对其安全运行具有重要意义,针对传统分析方法难以捕捉长期序列时序特征从而导致预测精度较低的问题,本文采用麻雀优化算法（SSA）和K调和均值算法（KHM）相结合对监测值进行聚类以捕捉序列时序特征,然后采用自适应噪声完备集合经验模态分解（CEEMDAN）等方法对聚类结果进行降噪处理,最后采用长短期记忆（LSTM）模型对序列进行预测。分析结果表明,本文所提出的聚类方法具有较好的长序列特征识别能力,结合基于CEEMDAN分解方法去除序列中存在的冗余信息,从而使LSTM模型能够更好地捕捉变形值的时序特性,进而提高预测精度。所提模型具有较好的精度和适应性,可为大坝变形预测提供一种有效方法。