基于改进量子粒子群算法的纸浆浓度控制系统

目的为了克服传统PID控制在具有大时滞性、非线性等特点的纸浆浓度控制系统中性能不足和参数调整困难等问题,研究参数在线调整的方法。方法在传统PID控制的基础上,结合量子粒子群仿生算法(QPSO),提出一种量子粒子群算法优化的传统PID控制器参数,并应用于纸浆浓度控制系统;同时对基本量子粒子群算法进行改进,引入交叉算子,并将该控制算法应用到纸浆浓度控制系统中,并与传统控制进行对比。结果与传统PID控制和基本量子粒子群优化的PID相比较,改进的优化算法能够得到更加令人满意的控制效果,具有系统超调量小、响应速度快、鲁棒性高等优良的性能。结论基于改进的量子粒子群优化算法的纸浆浓度控制系统可有效控制纸浆浓度,能够明显提高系统的控制精度等性能指标,更好地满足实际应用的要求。

Pulp Concentration Control System Based on Improved Quantum Particle Swarm Optimization Algorithm

The paper aims to research the method of online parameter adjustment to overcome the shortcomings of traditional PID control in pulp consistency control system with large time delay and non-linearity and the difficulty of parameter adjustment. Based on traditional PID control and in combination with quantum particle swarm optimization (QPSO) bionic algorithm, this paper proposed a traditional PID controller parameters optimized by QPSO and applied it to pulp concentration control system. At the same time, the basic QPSO algorithm was improved by introducing crossover operator, and the control algorithm was applied to pulp concentration control. The system was compared with traditional control. The result showed that compared with the traditional PID control and the basic quantum particle swarm optimization (QPSO) PID, the improved optimization algorithm could achieve more satisfactory control effect. The system had the advantages of small overshoot, fast response speed and high robustness. Pulp consistency control system based on improved quantum particle swarm optimization algorithm can effectively control pulp consistency, significantly improve the control accuracy and other performance indicators of the system, and better meet the requirements of practical application.