基于多目标优化PID的纸浆浓度控制系统

PID Pulp Concentration Control System Based on Multi-Objective Optimization

目的针对纸浆浓度PID控制系统在时滞性、稳定性、耦合性方面的不足,提出一种基于多目标优化的纸浆浓度PID控制方法。方法对纸浆生产工艺进行分析,结合纸浆浓度PID控制系统,设定多属性的决策变量,建立对应的目标函数和约束条件;从质量、产量、成本、环境等4个方面对纸浆浓度PID控制过程进行多目标优化,构建基于多目标优化的纸浆浓度PID控制模型;采用改进量子粒子群算法对多目标优化模型进行求解,获得Pareto最优纸浆浓度控制方案;将建模方法、优化算法、优选方法进行耦合,从而形成“建模-求解-优选”全过程的纸浆浓度控制方法。结果通过对纸浆浓度控制优化前后的决策变量进行比较分析可知,多目标优化PID控制方法在评价指标方面满足了质优、高产、低耗的多目标优化的可控性要求;相较于传统PID控制方法,IPSO-PID控制方法的响应速度更快,具有更好的鲁棒性;在PID参数优化方面,文中的优化模型整定控制参数在0.05 s内达到稳态阶段,稳态误差更低,具有更好的稳定性。结论在保证系统鲁棒性的同时,基于多目标优化算法的纸浆浓度PID控制系统可实现对纸浆浓度的精确性和稳定性控制,更好地满足实际工业生产的要求,确保纸张质量的品质。

The work aims to propose a PID pulp concentration control method based on multi-objective optimization for the shortcomings of pulp concentration PID control system in time lag,stability and coupling.The pulp production process was analyzed.Combined with the pulp concentration PID control system,multi-attribute decision variables were set,and the corresponding objective functions and constraints were established.The multi-objective optimization was carried out to the pulp concentration PID control process from quality,yield,cost and environment and the pulp concentration PID control model was built based on multi-objective optimization.The model was solved by improved quantum particle swarm algorithm to obtain the Pareto optimal pulp concentration control scheme.The modeling method,optimization algorithm and selection method were coupled to form the whole process of"modeling-solving-selection"pulp concentration control method.Through the comparison and analysis on decision variables before and after pulp concentration control optimization, the multi-objective optimized PID control method met the controllability requirements ofmulti-objective optimization with good quality, high yield and low consumption in terms of evaluation indexes. IPSO-PIDcontrol method had faster response speed and better robustness compared with the traditional PID control method. Interms of PID parameter optimization, the optimization model set the control parameters in the steady-state stage within0.05 seconds, with lower steady-state error and better stability. While ensuring the robustness of the system, the PID pulpconcentration control system based on multi-objective optimization algorithm can achieve accurate and stable control ofpulp concentration, meet the requirements of actual industrial production better, and ensure the quality of paper.