基于粒子群优化算法的船舶动力定位云模型控制器设计

Dynamic ship positioning cloud model controller design based onparticle swarm optimization algorithm

摘要:

云模型控制理论是智能控制学科的新兴领域,因此如何扩展云模型的应用范围并使其走向工程化实用化成为其研究重点。针对船舶运动模型具有不确定性和外部扰动随机性等特点,尝试将云模型应用于船舶动力定位的控制过程中。由于云模型控制器存在参数难以整定的问题,提出了基于粒子群算法的优化设计方法。针对标准粒子群优化算法容易出现早熟收敛的问题,引入自适应粒子群优化算法。仿真研究表明云模型控制及粒子群优化的可行性和有效性。

Abstract:

Cloud model control theory is emerging field in the intelligent control subject, consequently the research on it focuses on enlarging its application and making it be engineering and practical. As the ship motion model contains uncertainty and random external disturbance, tried to apply cloud model to the dynamic positioning control process. An improved design based on particle swarm optimization (PSO) was proposed to overcome the difficulty in parameters regulation of cloud model controller. In order to avoid premature convergence in standard PSO, the adaptive PSO (APSO) was introduced. Simulation results verify that the designed cloud model controller and particle swarm optimization are feasible and effective.