基于混合混合Regrouping PSO-GA的子优化网络控制系统设计与随机丢包
Design of Hybrid Regrouping PSO-GA based Sub-optimal Networked Control System with Random Packet Losses
AI总结：
本文提出了一种新的方法，用于设计具有随机丢包的网络控制系统中的子优化状态反馈调节器，通过Lyapunov技术设计最优调节器增益，结合GA和内点法减少BMIs计算复杂性，并利用RegPSO优化加权矩阵以提高稳定性和跟踪性能。
摘要：
本文提出了一种新的方法，用于设计具有随机丢包的网络控制系统中的子优化状态反馈调节器。通过Lyapunov技术，利用植物的名义离散时间模型设计最优调节器增益，产生一组双线性矩阵不等式（BMIs）。为减少BMIs的计算复杂性，采用基于遗传算法（GA）的方法结合标准内点法处理LMIs。然后采用Regrouping粒子群优化（RegPSO）方法，优化状态反馈调节器设计中的加权矩阵，通过GA基于的稳定性检查标准（即BMIs）进行传递。本文提出的混合优化方法不仅减少了最优稳定增益选择的可行性检查条件的计算难度，还最小化了其他时域性能标准，如预期的设定点跟踪误差的期望值，通过基于最优加权选择的LQR设计来优化名义系统。
英文摘要：
In this paper, a new approach has been presented to design sub-optimal state feedback regulators over Networked Control Systems (NCS) with random packet losses. The optimal regulator gains, producing guaranteed stability are designed with the nominal discrete time model of a plant using Lyapunov technique which produces a few set of Bilinear Matrix Inequalities (BMIs). In order to reduce the computational complexity of the BMIs, a Genetic Algorithm (GA) based approach coupled with the standard interior point methods for LMIs has been adopted. A Regrouping Particle Swarm Optimization (RegPSO) based method is then employed to optimally choose the weighting matrices for the state feedback regulator design that gets passed through the GA based stability checking criteria i.e. the BMIs. This hybrid optimization methodology put forward in this paper not only reduces the computational difficulty of the feasibility checking condition for optimum stabilizing gain selection but also minimizes other time domain performance criteria like expected value of the set-point tracking error with optimum weight selection based LQR design for the nominal system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
