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矿井提升机在频繁的重复运动中具有随时间变化、非线性的特点,传统PID控制器难以达到理想的控制效果。提出了一种改进的单神经元PID控制器,利用神经元的自学习、自组织能力,通过对权值的在线调整达到对PID参数在线调整的目的,同时采用粒子群优化算法对单神经元PID控制器参数进行优化。仿真结果表明,改进的单神经元PID控制器具有良好的控制性能,提高了矿井提升机的稳定性。
Mine hoist with frequent repetitive movements with time, non-linear characteristics of the traditional PID controller is difficult to achieve the desired control effect. An improved single neuron PID controller is proposed, which utilizes the self-learning and self-organizing ability of neurons, adjusts the PID parameters on-line through the online weight adjustment, and uses the particle swarm optimization algorithm to optimize the single neuron PID controller parameters to optimize. The simulation results show that the improved single-neuron PID controller has good control performance and improves the stability of mine hoist.