Abstract: The ideal varying process of the individual tuning nonlinear PID controller concerning error is analyzed and a nonlinear PID controller is constructed.The conventional parameter optimization of PID controller is easy to produce surge and big overshoot，and therefore a method of random selection of optimal individual is introduced into Quantum-behaved Particle Swarm Optimization（QPSO） and an improved QPSO（GQPSO） for the parameter optimization of nonlinear PID controller is proposed.The comparison of GQPSO，PSO and QPSO based on benchmark functions is given.Finally，a typical example is given to illustrate the design procedure and exhibit the effectiveness of the proposed method via a comparison study with existing Z-N，PSO and QPSO approaches.

Key words: Quantum-behaved Particle Swarm Optimization（QPSO）, random selection, nonlinear PID controller, parameter optimization

摘要： 分析了非线性PID控制器各部分参数对于误差的理想变化过程，构造出一种非线性PID控制器；整定参数较多时，传统的参数优化方法容易产生振荡和较大的超调量，在分析量子粒子群算法（QPSO）的基础上，引入了随机选择最优个体的思想，提出使用改进的量子粒子群算法（GQPSO）优化非线性PID控制器参数。将改进量子粒子群算法与量子粒子群算法、粒子群算法通过benchmark测试函数进行了比较。最后，通过典型传递函数实例，分别使用Z-N、PSO、QPSO方法和改进的量子粒子群算法进行了PID控制器参数优化设计，并对结果进行了分析。

关键词: 量子粒子群算法, 随机选择, 非线性PID控制器, 参数优化