Liao Zirui, Wang Shaoping, Shi Jian, Li Ming, Zhang Yuwei, Sun Zhiyong
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo, 315800, China; Shenyuan Honors College, Beihang University, Beijing, 100191, China.
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo, 315800, China.
ISA Trans. 2024 Jun;149:1-15. doi: 10.1016/j.isatra.2024.04.015. Epub 2024 Apr 16.
This work presents a resilient distributed optimization algorithm based on the event-triggering mechanism for cyber-physical systems (CPSs) to optimize an average of convex cost functions corresponding to multiple agents under adversarial environments. Two attack scenarios, including the f-total (each agent is affected by at most f malicious agents in the whole network) and the f-local (each agent is affected by at most f malicious agents in its in-neighbor set) attacks are considered. Subsequently, the convergence conditions under these two attack scenarios are provided, respectively, both of which guarantee that the state values of benign agents converge to a bounded error range. The optimality conditions are also presented by theoretical analysis, which guarantee that the state values of benign agents converge to a safety interval constructed by local optimal values under certain graph conditions, despite the misbehavior of malicious agents. In addition, four numerical examples are presented to show the effectiveness and superiority of the event-triggering resilient distributed optimization (RDO-E) algorithm. Compared to existing resilient algorithms, the proposed method achieves resilient distributed optimization with higher accuracy and less demanding communication overheads. Finally, by applying the proposed method to the multi-microgrid system, a resilient economic dispatch problem (REDP) is successfully solved, which validates the practical viability of the RDO-E algorithm.
这项工作提出了一种基于事件触发机制的弹性分布式优化算法，用于优化网络物理系统（CPS）在对抗环境下多个智能体对应的凸成本函数的平均值。考虑了两种攻击场景，包括f-全局攻击（每个智能体在整个网络中最多受到f个恶意智能体的影响）和f-局部攻击（每个智能体在其入邻居集中最多受到f个恶意智能体的影响）。随后，分别给出了这两种攻击场景下的收敛条件，这两个条件都保证良性智能体的状态值收敛到有界误差范围内。通过理论分析还给出了最优性条件，这保证了在某些图条件下，尽管存在恶意智能体的不当行为，良性智能体的状态值仍收敛到由局部最优值构成的安全区间。此外，给出了四个数值例子来说明事件触发弹性分布式优化（RDO-E）算法的有效性和优越性。与现有的弹性算法相比，所提出的方法以更高的精度和更低的通信开销实现了弹性分布式优化。最后，通过将所提出的方法应用于多微电网系统，成功解决了一个弹性经济调度问题（REDP），这验证了RDO-E算法的实际可行性。
