直驱泵系统的单神经元PID+前馈控制策略

    Single Neuron PID+Feedforward Controller of a Direct Drive Pump System

    • 摘要: 直线电机驱动柱塞泵(简称直驱泵)系统具有较强的非线性,且系统在运行过程中需要根据不同的流量需求及负载状况改变直线电机的运动状态.传统PID控制策略的控制参数需要人工手动调节且整定后不会随工况改变做出相应调整.针对传统PID控制算法控制效果不佳的缺点,提出了一种基于可编程多轴运动控制器(programmable multi-axes controller,PMAC)的单神经元PID+前馈控制策略.单神经元PID+前馈控制以单神经元模块的输出作用律作为控制量的补偿量加入到控制算法中,具有神经网络的学习能力、适应能力和PID控制的广泛适应性.对神经元比例系数K1采用人工调整的配置方式进行改进,使K1可根据误差绝对值自动在线调节并将K1值限定在一个合适的范围内.通过MATLAB/Simulink建模仿真并利用Turbo PMAC运动控制器的"开放伺服"功能在直驱泵控制系统平台对此控制策略进行试验验证.结果表明,在相同情况下,与控制器内建的控制策略相比,单神经元PID+前馈控制策略下系统响应更快、超调更低、跟随误差更小,系统的动态性能和鲁棒性都得到进一步提升.

       

      Abstract: The linear motor driven piston pump (direct drive pump) system has strong nonlinearity. According to different flow rate demands and load conditions, the motion state of linear motor in the system needs to be changed during the running process. Manual adjustment of the control parameters is always needed in the traditional PID control strategy, and the control parameters do not change with the change of working conditions. Owing to the shortcomings of traditional PID control algorithm, a single neuron PID+ feedforward control algorithm based on programmable multi-axes controller (PMAC) was proposed. The output of the single neuron module which was used as the compensation dosage of the control variable, was added to the control algorithm. This algorithm has certain learning ability, adaptability of neural network algorithm and wide adaptability of PID control. The manual adjusted configuration was used to improve the neuron proportional coefficient K1. As a result, the K1 automatically changed with the absolute error value, and it was limited to a suitable range. Based on the modeling and simulation through MATLAB/Simulink, the "open servo" function of Turbo PMAC motion controller was used to experimentally validate the control strategy in the direct drive pump control system platform. Results show that compared with the control strategy built by the controller, the single neuron PID+ feedforward control strategy has a faster system response, lower overshoot and smaller following error. Besides, the dynamic performance and robustness of the system are also further improved.

       

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