Identification of acceleration harmonic for an electro-hydraulic servo shaking table based on Kalman filter

Abstract

Non-linearities commonly exist in an electro-hydraulic servo shaking table, causing acceleration harmonics distortion when the shaking table is excited by a sinusoidal acceleration signal, because its acceleration response includes higher harmonics, which lower the control performance for an electro-hydraulic servo shaking table. To cancel the harmonics in the system response, thus to improve the shaking table performance, we need to know about the harmonics information. An identification algorithm is developed here based on a Kalman filter for dynamically tracking the acceleration harmonics for the electro-hydraulic servo shaking table. A linear system in state space is modelled. The system acceleration response is applied as an observation value and is imported to the Kalman filter, which recursively estimates the state vector of the linear system. The amplitude and phase of each harmonic are calculated from the estimated state vector, and their estimated values are validated. A simulation example is presented and experiments were performed on the electro-hydraulic servo shaking table. Both results show a good estimation performance of the proposed acceleration harmonic identification algorithm.

Sine phase compensation combining an amplitude phase controller and a discrete feed-forward compensator for electro-hydraulic shaking tables

“…Therefore, it is possible to eliminate the phase delay as long as the acceleration response signal obtain a corresponding phase advance φ , and the reference signal will be converted as follows (Yao et al, 2013)…”

“…The latter is caused by the time of the data transmission and sampled-data processing. Furthermore, a simple scheme, amplitude-phase regulation, was proposed to improve the phenomenon using a least mean square (LMS) adaptive filter, which made the output signal track the input efficiently (Yao et al, 2013). For minimizing the effect of the system delay, Chen and Ricles (2009) designed an actuator delay compensator in real time testing by using the discrete control theory.…”

Neuro-fuzzy iterative learning control for 4-poster test rig

“…nonlinear) or external disturbances and improve the tracking performance, repetitive control methods are available for tracking periodical reference signals (Plummer et al, 2005; Thoen, 1992). Yao et al (2013) and Yao et al (2017) established control methods to cancel acceleration harmonics that occur based on hydraulic nonlinearities. Chen et al (2017) offer a cerebellar model articulation controller for material strength testing to reduce the nonlinearity effect of the system.…”

Force tracking control of an electro-hydraulic control loading system on a flight simulator using inverse model control and a damping compensator

“…If the disturbance force is ignored, the inner loop of the EHCLS can be seen as a typical force closed-loop, and some conventional control methods can be employed to improve its replicating ability. A linear feedback controller is often employed to keep stable tracking performance of electro-hydraulic actuator systems, such as the proportionalintegral-derivative (PID) controller (Shen et al, 2011;Yang et al, 2012), the three variables feedback controller (Shen et al, 2013(Shen et al, , 2016Xu et al, 2008;Yao et al, 2013a) and feedback linearization (Plummer, 1997). However, nonlinear properties of a hydraulic force servo system always limit force tracking accuracy.…”