Optimal Control of Nuclear Power Plant Steam Generator Based on GMFAC
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摘要: 针对蒸汽发生器水位"虚假水位"等问题,在无模型自适应控制(MFAC)理论的基础上提出高"泛模型"无模型自适应控制(GMFAC)方法,并设计用于蒸汽发生器水位优化控制的控制器。为解决无模型自适应控制参数优化问题,采用了一种基于动物行为的群体智能优化算法——人工鱼群算法(AFSA)。为了避免陷入局部最优,提高收敛速度,同时采用一种改进的AFSA算法(PSO-AFSA),参考粒子群(PSO)算法的自身认知与群体认知行为,定义鱼群的生活行为,以提高算法的精度,达到快速获得全局最优的目标。仿真结果表明:人工鱼群算法优化后的GMFAC具有更加优良的性能指标和抗扰能力。Abstract: According to the nonlinear control system of steam generator water level, large lag and the "false water level" caused by load changes and other issues, based on the model free adaptive control（MFAC） theory, an improved model free adaptive control（GMFAC） theory which is based on high"universal model" is proposed, and the relevant controller is designed to control the water level of steam generator.For the model free adaptive control parameter optimization problem,A swarm intelligence optimization algorithm based on animal behavior — artificial fish swarm algorithm（AFSA） is proposed.In order to avoid the local optimum and improve the convergence rate, an improved AFSA algorithm（PSO-AFSA） is proposed.In order to improve the accuracy of the algorithm and to improve the accuracy of the algorithm, a reference particle swarm optimization（PSO） algorithm is defined to improve the accuracy of the algorithm.The simulation results show that the GMFAC has better performance and disturbance rejection ability after optimization of the artificial fish swarm algorithm.
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