基于蝙蝠算法的PID控制参数优化技术与仿真研究

PID control parameter optimization technology and simulation research based on bat algorithm

PID参数整定是PID控制中的一个重要环节,传统的PID参数整定方法已经不能完全适用。为提高PID参数优化精度,解决传统PID参数整定时产生的误差较大问题,将蝙蝠算法引入控制系统中优化PID控制参数。通过MATLAB仿真,比较蝙蝠算法、粒子群优化算法和增量式PID控制算法对控制参数优化的性能。实验结果表明:在函数寻优测试中,与遗传算法、粒子群优化算法相比,蝙蝠算法能防止陷入局部最优,使种群更加稳定并达到更好的收敛速度和寻优精度;在PID控制参数优化中,与粒子群优化算法、增量式PID控制算法相比,蝙蝠算法优化PID控制参数的实际输出曲线最贴近理论输出曲线,稳定性更好。

PID parameter tuning is an important link in PID control,and the traditional PID parameter tuning method can no longer be fully adapted.In order to improve the accuracy of PID parameter optimization and solve the problem of large error caused by traditional PID parameter tuning,the bat algorithm was introduced into the PID control system.Through MATLAB simulation test,the performance of control parameter optimization of bat algorithm,particle swarm optimization algorithm and incremental PID control algorithm is compared.The experimental results show that in the function optimization test,compared with genetic algorithm and particle swarm optimization,bat algorithm can prevent falling into the local optimum,make the population more stable and achieve better rate of convergence and optimization accuracy.In the optimization of PID control parameters,compared with the optimization results of particle swarm optimization and incremental PID control algorithm,the actual output curve of bat algorithm optimized PID control parameters is closest to the theoretical output curve,and has better stability.