医院大型电梯电力系统的PLC控制方法优化

医院大型电梯电力系统在实际运行的过程中具有很高的非线性和时变性,使大型电梯电力系统PLC控制效果不佳,当前方法采用线性学习法,无法适应运行环境的不确定性,造成电力系统控制的不稳定性。提出一种基于BP神经网络PID控制算法的医院大型电梯电力系统的PLC控制优化方法,将PID算法作为PLC的软件设计部分,通过比例、积分与微分这三种控制作用的合理调配,形成相互关系。鉴于PID控制算法调节时间长、超调量大等弊端,采用BP神经网络对其进行优化。利用BP神经网络的自学习和加权系数的调整,使BP神经网络输出最优大型电梯电力系统控制规律下的PID控制算法的参数,实现医院大型电梯电力系统的稳定控制。实验结果表明,所提方法具有很高的控制稳定性和鲁邦性,综合性能较强。

Large hospital elevator PLC control method of power system optimization

Large hospital elevator power system in the process of actual operation are highly nonlinear and time-varying, the large elevator power system, PLC control, the current method, linear learning method is adopted, cannot adapt to the uncertainty of operating environment, caused the instability of power system control. Presents a based on BP neural network PID control algorithm of large hospital elevator PLC control of the power system optimization method, the PID algorithm as the PLC software design part, through proportion, integral and differential control action of the three reasonable allocate, form a relationship. With PID control algorithm is long time to adjust, and large amount of overshoot, using BP neural network for the optimization. Using BP neural network self-learning and weighted coefficient of adjustment, the BP neural network output under the large elevator power system optimal control law of the parameters of the PID control algorithm, realization of large hospital elevator power system stability control. The experimental results show that the proposed method has high control stability and ruban yogarajah, comprehensive performance is stronger.