基于改进PSO算法的PID优化用于黑液液位控制

Application of the PID Parameters Optimization Based on Improved Particle Swarm Optimization in Level Control of Black Liquor

制浆过程中碱回收蒸发工段黑液液位控制直接影响着黑液浓度和蒸发效率。针对黑液液位非线性、大时滞及时变性的特点,传统PID方法控制精度较低,使用标准粒子群算法可以优化PID参数,提高精度,但是收敛速度慢,整定时间长。针对这些问题,采用改进的粒子群算法来整定PID参数,通过动态调整惯性因子和加速因子,以及改进收敛准则等方法来提高粒子群算法的全局寻优能力和收敛速度,并在MATLAB/SIMULINK仿真实验平台上,比较了传统PID方法、标准PSO算法和改进PSO算法对黑液液位的控制效果,结果表明改进PSO算法优化的PID控制缩短了调节时间,降低了超调量,说明改进粒子群算法优化后的黑液液位PID控制具有更快的响应速度和更好的鲁棒性,有效地提高了控制质量。

Black liquor level control in evaporation process of alkali recovery in pulping directly affects the black liquor concentration and evaporation efficiency, In view of the black liquor level characterized of non-linear, large delay and time-varying, The traditional PID method has low accuracy in controlling objects. The standard particle swarm optimization algorithm can optimize PID parameters so as to improve the accuracy, but the convergence speed is slow and the tuning time is long. In order to address these problems, the improved particle swarm optimization algorithm for tuning PID parameters, such as the dynamical adjustment of inertia factor, acceleration factor, the improved convergence criterion can be used to enhance the global optimization ability and convergence rate of particle swarm optimization algorithm. The simulation results in Matlab/Simulink platform showed that the improved particle swarm optimization algorithm for tuning PID parameters had greatly improved the adjustment time and the overshoot compared with the traditional PID method and the standard particle swarm optimization algorithm. The simulation results show that the proposed method has faster response speed and better robustness than the Z-N tuning and traditional genetic algorithm which indicate high controlling quality enhancing.