基于改进粒子群优化算法的PID控制器整定

由传统的Z-N(Ziegler-Nichols)整定公式得出的PID参数,不能得到最佳的控制性能.为此,本文提出一种基于适应度指数定标和边界缓冲墙相结合的改进型粒子群算法,应用于PID参数的整定.首先采用适应度指数定标的选中概率,挑选出粒子进行随机变异;其次对越界的粒子进行缓冲,保证粒子落在寻优空间内以增加粒子种群多样性,同时调整种群粒子个数、社会和认知因子以提高寻优效率.在仿真实验中,将改进的粒子群算法分别应用于5种不同的工业过程,整定他们的PID参数.对误差绝对值乘以时间积分的性能指标(ITAE)做最小化,得到了相应的PID参数,验证了这里提出的改进型粒子群算法的有效性.

Tuning of PID controller based on improved particle-swarm-optimization

Because the classical PID parameter settings obtained by Z-N(Ziegler-Nichols) method usually fail to achieve the best control performances, we propose an improved particle swarm optimization(IPSO) algorithm with fitness exponential scale and border buffer wall for tuning the PID parameters. First, by the selection-probability of the fitness exponential scale, we select the underbred particles for random mutations. Secondly, we employ the border buffer wall to block the slopping-over particles, making them to fall in the explored space of optima to enhance the diversity of the particle swarm.Meanwhile, by modifying the number of swarm particles as well as the social and cognitive factors, we improve the efficiency of optimum searching. In the simulation experiments, we apply the IPSO algorithm to the PID parameter tuningfor 5 different industrial process models, the corresponding optimal PID parameters are obtained under the criterion of minimal integral-time-weighted-absolute error(ITAE). The effectiveness of the proposed improved particle swarm optimization(IPSO) algorithm is validated.