基于无模型自适应算法的磁流变液水分控制

Moisture content control strategy of magnetorheological fluid based on model-free adaptive control algorithm

磁流变抛光是一种去除效率稳定、无亚表面损伤的超精密加工工艺,然而,在抛光过程中,磁流变液的水分损失会改变抛光工具的特性,从而影响去除函数的稳定性。现有的水分控制策略受到磁流变液循环系统大时延、时变扰动的影响,导致水分含量存在周期性波动,使去除函数发生周期时变,进而影响加工质量与精度。本研究建立了磁流变液循环系统的传递函数模型,开展了系统特性分析,并据此设计了基于全格式动态线性化的无模型自适应控制算法,该算法能够实现非线性系统的参数自适应控制,有效抑制因时变扰动和时延引起的水分波动,为抛光过程中水分含量的稳定控制提供了一种简单有效、适用性强的控制策略。实验结果显示,采用FFDL-MFAC控制算法时,磁流变液水分波动的峰谷值(Peak-valley Value,PV)仅为0.06%,相较于使用PID减少了40%,误差绝对值的积分(Integral value of Absolute Er‐ror,IAE)减少了58.1%。有效提升了抛光过程中磁流变液水分含量的稳定性。

Magnetorheological finishing is an ultra-precision machining process with stable removal efficiency and no subsurface damage.However,the water loss of magnetorheological fluid(MR fluid)in the polishing process will change the properties of polishing tools,thus affecting the stability of removal function.The existing water control strategy is affected by the large time delay and time-varying disturbance of MR Fluid circulation system,which leads to the periodic fluctuation of water content and the periodic timevarying removal function,thus affecting the machining quality and accuracy.In this study,the transfer function model of MR fluid circulation system was established,and the characteristics o f the system were analyzed.based on this,a Model-free adaptive control based on full form dynamic linearization(FFDLMFAC)algorithm is designed.The algorithm can realize the parameter adaptive control of nonlinear system,effectively suppress the water fluctuation caused by time-varying disturbance and delay,and provide a simple,effective and applicable control strategy for the stable control of water content in the polishing process.The experimental results show that when FFDL-MFAC control algorithm is adopted,the Peakvalley value(PV)of the water fluctuation of MR Fluid is only 0.06%,which is reduced by 40%compared with PID.The Integral value of absolute error(IAE)was reduced by 58.1%.T he stability of w ater content of MR fluid in polishing process is effectively improved.