基于粒子群算法优化BP神经网络的PID控制算法  被引量:11

The PID control algorithm based on particle swarm optimization optimized BP neural network

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作  者:曾雄飞 ZENG Xiongfei(Zhongshan Jixin Lock Core Co.,Ltd.,Zhongshan 528400,China)

机构地区:[1]中山市基信锁芯有限公司,广东中山528400

出  处:《电子设计工程》2022年第11期69-73,78,共6页Electronic Design Engineering

摘  要:传统比例-积分-微分(Proportion Integral Derivative,PID)控制器存在参数整定困难,不能在线实时调整以及面对复杂非线性系统时应用效果不佳等问题,提出一种基于粒子群算法(Particle Swarm Optimization,PSO)优化的反向传播(Back Propagation,BP)神经网络PID控制方法。将BP神经网络与PID控制器相结合,利用BP神经网络的自适应学习能力在线实时调整PID控制参数,提升系统稳定性,针对BP-PID自学习过程中容易陷入局部极小值问题,利用改进的PSO算法对其进行优化,确保BP-PID系统收敛于全局最优解。基于仿真数据开展实验,结果表明,所提方法能够有效提升系统的控制精度和控制稳定度。Traditional Proportion Integral Derivative(PID)controllers have problems in parameter tuning,inability to adjust online and in real time,and poor application effects when facing complex nonlinear systems.A Particle Swarm Optimization(PSO)optimized Back Propagation(BP)neural network PID control method is proposed.The BP neural network is combined with the PID controller,and the adaptive learning ability of the BP neural network is used to adjust the PID control parameters online in real time to improve System stability,for the BP-PID self-learning process,it is easy to fall into the local minimum problem.The improved PSO algorithm is used to optimize it to ensure that the BP-PID system converges to the global optimal solution.The experiments are carried out based on simulation data,and the results show that the proposed method can effectively improve the system control accuracy and control stability.

关 键 词:比例-积分-微分控制器 神经网络 模型优化 粒子群算法 

分 类 号:TN3[电子电信—物理电子学]

 

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