A Hybrid Quantum Clone Evolutionary Algorithm-Based Scheduling Optimization in a Networked Learning Control System

摘要：

Control and network performance rely on the design of system architecture, control algorithm and scheduling of network information. In this paper, a two-layer networked learning control system (NLCS) architecture is introduced, achieving better control performance, better interference rejection and increasing the adaptability to varying environment. We establish a multi-objective optimization (MOO) based on Hybrid Quantum Clone Evolutionary Algorithm (HQCEA) with rules of expert knowledge describing for the control performance and bandwidth requirements in the two-layer NLCS to dynamically allocate bandwidth of each control loop, aiming at realizing maximization of control performance and minimization of bandwidth consumption. Such theoretical results are confirmed by the simulations of the algorithm.

关键词： NLCS HQCEA Multi-objective optimization Bandwidth

作者: Lijun Xu Minrui Fei

作者单位: Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai 200072, China

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3632-3637

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）