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arXiv 1603.02426eess.SYcs.SY
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使用粒子群优化-差分进化-线性矩阵不等式混合算法的鲁棒静态输出反馈控制

Robust static output feedback control using a Particle Swarm Optimization-DE-linear matrix inequality algorithm hybrid algorithm

Kim Il Sung University(金日成综合大学)

IlGon Ho, YongHyok Ri

AI总结:

本文提出一种混合算法,用于解决连续多顶点不确定系统的鲁棒静态输出反馈控制问题,结合粒子群优化-差分进化与线性矩阵不等式方法,提升迭代方法的收敛速度和精度。

摘要:

在多目标框架下,本文提出了一种混合算法,用于解决连续多顶点不确定系统的鲁棒静态输出反馈控制问题。通过将粒子群优化与差分进化混合算法与线性矩阵不等式方法相结合,提出了一种新的混合算法,用于解决具有双线性矩阵不等式约束的优化问题。粒子群优化-差分进化混合算法用于生成控制器群体,线性矩阵不等式方法用于优化系统性能指标。以混合H2/H∞控制问题为例,详细阐述了求解鲁棒静态输出反馈控制问题的算法。仿真结果表明,所提出的混合算法提高了迭代方法的收敛速度和精度,优于原始差分进化-线性矩阵不等式算法。

英文摘要:

Under the multi-objective framework, this paper presents a hybrid algorithm to solve robust static output feedback control problem for continuous poly-topic uncertain system. To obtain static output feedback gain, a new hybrid algorithm is proposed by combination of a hybrid algorithm of the Particle Swarm Optimization and Differential Evolution (DE), and the linear matrix inequality method. The proposed algorithm is used to solve a optimization problem with a bi-linear matrix inequality constraints. The Particle Swarm Optimization-DE hybrid algorithm was used to obtain a population of controllers, and linear matrix inequality approach was used to optimize a performance criterion of the system. Taking a hybrid H 2/H infinite control problem as example, the detailed algorithm is presented to solve robust static output feedback control problem. The simulation results show that the proposed hybrid algorithm improves the convergence rate and accuracy of iterative methods and the original DE-linear matrix inequality algorithm.

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。

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