Translational shaking tests achieved on a 6-DOF hydraulic parallel manipulator

Abstract

Purpose – The paper aims to achieve translational shaking tests on a 6-DOF hydraulic parallel manipulator. Shaking tests are commonly performed on shaking tables, which are generally used for small motion ranges and are usually financially costly. The research is required to generate shaking motions in three translational directions for a specimen for shaking tests, but it also needs to produce 6-degree of freedom (DOF) motions with large motion ranges. Design/methodology/approach – A hydraulic 6-DOF (degree of freedom) parallel manipulator is applied to achieve this goal. The link-space control is adopted for the manipulator, and PID controller and feed-forward controller are used for each loop of the system. A hybrid reference signal generator is proposed by using a shaking controller, which is developed to convert the shaking motion into position signal. The converted result is directly added to the pose signal. The whole real-time control system is realized by using MATLAB xPC Target. Findings – The developed method is verified on the hydraulic 6-DOF parallel manipulator with specimen. Experiments show very promising results that the proposed technology is really applicable to perform translational shaking tests on the hydraulic parallel manipulator. Originality/value – A simple yet efficient solution is proposed that allows shaking tests in three translational directions performed on the hydraulic 6-DOF parallel manipulator with wide motion ranges. The paper presents a state-of-the-art related to the applications of parallel robots in several fields of technology.

Acceleration waveform replication on six-degree-of-freedom redundant electro-hydraulic shaking tables using an inverse model controller with a modelling error

“…However, the measured acceleration waveform with the TVC remain imperfect because acceleration waveform tracking performances may be effected by non-linear properties of electro-hydraulic servo systems (Kenta et al, 2009; Niksefat and Sepehri, 2001; Yao et al, 2016), especially the dynamic characteristics of servo-valves and hydraulic actuators (Shen et al, 2016; Zhao et al, 2016a, 2016b). Hence, a feed-forward inverse model (FFIM) compensator is employed to improve the acceleration waveform tracking accuracy by compensating for the dynamic characteristics of servo-valves and hydraulic actuators.…”

Identification of Acceleration Harmonics for a Hydraulic Shaking Table by Using Hopfield Neural Network

“…The TVC scheme can it can adjust the system robustness and disturbance suppression performance, and improve the reference tracking performance. The whole control program is achieved by using xPC Target, which is a real-time workshop in Matlab [25]. The system shaking response is shown in Figure 3 for the case when the acceleration input is 3 sin(2 8t) m/s 2 .…”