基于遗传蚁群混合策略的PID控制器参数整定

Research on Parameters Adjustment of PID Controller Based on ACO-GA Hybrid Strategy

传统的PID控制器参数整定方法或者需要对被控过程和控制规律有全面的先验知识,或者建立在要求具有连续导数的光滑搜索空间的基础上,或者容易"早熟"和收敛速度较慢。文中结合蚁群算法(ACO)和遗传算法(GA)各自的优点,提出了一种新型的蚁群算法(ACO)-遗传算法(GA)混合优化策略(ACO-GA)的PID参数优化方法。仿真应用研究表明:与非线性设计方法(NCD)以及蚁群算法相比,ACO-GA优化策略具有更强的寻优能力和快速收敛能力,是一种适用于工程应用的参数寻优方法。

Traditional parameters adjustments of PID need the general experiential knowledge on control process and control law, or have continuous differential coefficient search space, or can be premature and convergent slowly. So, absorbing the merits of ant colony optimization （ACO） algorithm and genetic algorithm （GA）, a new ACO-GA hybrid optimization strategy is developed to optimize PID parameters. The simulation shows; ACO-GA hybrid optimization has more excellent performance in finding best solution and convergence than nonlinear control design and ACO, and this method can be adapt to engineering application.