论文部分内容阅读
针对孵化系统复杂的动态非线性特性,提出一种基于粒子群优化的模糊控制算法,该算法针对模糊控制器量化因子参数调节的困难,采用PSO的惯性系数的自适应调整机制,用以加速优化算法的收敛性和维持群体的多样性,以寻优模糊控制器量化因子参数,将该方法应用于孵化过程,较好的实现了温度、湿度和含氧量的稳定控制。仿真和实际运行结果表明了所提出的算法的有效性和优越性。
Aiming at the complex dynamic nonlinear characteristics of hatching system, a fuzzy control algorithm based on Particle Swarm Optimization (PSO) is proposed. According to the difficulty of adjusting parameters of fuzzy controller, an adaptive adjustment mechanism of PSO inertia coefficient is adopted to speed up the optimization The convergence of the algorithm and the diversity of the population are maintained to find out the parameters of the fuzzy controller quantification factor. The method is applied to the hatching process, and the stable control of temperature, humidity and oxygen content is well achieved. The simulation and actual operation show the effectiveness and superiority of the proposed algorithm.