Robust tracking control for micro machine tools with load uncertainties

Abstract

The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized linear motor drive precision stage. The tracking behavior of a direct drive design is prone to uncertainties such as model parameter variations and disturbances. Robust optimal tracking controller design for this kind of precision stages with mass and damping ratio uncertainties was researched. The mass and damping ratio uncertainties were modeled as the structured parametric uncertainty model. An identification method for obtaining the parametric uncertainties was developed by using unbiased least square technique. The instantaneous frequency bandwidth of the external disturbance signals was analyzed by using short time Fourier transform technique. A two loop tracking control strategy that combines the μ-synthesis and the disturbance observer (DOB) techniques was proposed. The μ-synthesis technique was used to design robust optimal controllers based on structured uncertainty models. By complementing the μ controller, the DOB was applied to further improving the disturbance rejection performance. To evaluate the positioning performance of the proposed control strategy, the comparative experiments were conducted on a prototype micro milling machine among four control schemes: the proposed two-loop tracking control, the single loop μ control, the PID control and the PID with DOB control. The disturbance rejection performances, the root mean square (RMS) tracking errors and the performance robustness of different control schemes were studied. The results reveal that the proposed control scheme has the best positioning performance. It reduces the maximal errors caused by disturbance forces such as friction force by 60% and the RMS errors by 63.4% compared with the PID control. Compared to PID with DOB control, it reduces the RMS errors by 29.6%.

A Control Method Based on a Simple Dynamic Optimizer: An Application to Micromachines with Friction

“…Regarding this point, many control strategies have been proposed. Among these, we have those based on PID schemes [ 3 ], modern control theory [ 2 ], robust analysis [ 5 , 6 ], neuronal theory together with terminal sliding mode control [ 7 ], and so on. Here, we propose a new control scheme based on a kind of dynamic optimizer [ 8 ].…”

“…However, this technique is based on a well-identified friction model [ 23 ]. Techniques based on data training require many stages of experimentation for friction compensation design [ 6 ]. Other techniques require a pre-digital filter design, based on fitting system parameter data through experimentation [ 24 ].…”

Analysis and implementation of an artificial vision method for micropositioning based on a camera-screen system

“…Dentro del campo del micromecanizado, cabe indicar, que el objetivo no consiste sólo en una miniaturización sencilla de los procesos que utilizan herramientas macroscópicas [16]. Existen una gran cantidad de métodos de micro-mecanizado muy flexibles, tales como taladrado, fresado, ranurado, litografía y láser.…”