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

2016 ◽  
Vol 40 (3) ◽  
pp. 968-986 ◽  
Author(s):  
Gang Shen ◽  
Zhen-Cai Zhu ◽  
Xiang Li ◽  
Qing-Guo Wang ◽  
Ge Li ◽  
...  

A redundant electro-hydraulic shaking table (REST) of six degrees-of-freedom (6 DOFs) with eight hydraulic actuators is an essential experimental tool in many industrial applications for real-time simulation of actual vibrations, such as structural vibration, earthquake simulation and fatigue testing. In order to obtain a high-fidelity acceleration waveform on the REST, a feed-forward inverse model (FFIM) controller with a modelling error compensator is proposed in this study. A recursive extended least-squares algorithm is employed to identify an acceleration closed-loop transfer function of the REST. A zero phase error compensation technology is employed to guarantee stability of the designed FFIM because the identified acceleration closed-loop transfer function is a typical non-minimum phase system and its direct inverse transfer function is unstable. The modelling error compensator is designed to compensate for the modelling error between the identified transfer function and the actual experimental REST, which deteriorates the acceleration waveform replication accuracy of the REST. A 6 DOF REST experimental system was used to verify the proposed controller. Experimental results demonstrated that the proposed controller gave satisfactory acceleration tracking performances on the REST.

Author(s):  
Meng-Sang Chew ◽  
Theeraphong Wongratanaphisan

Abstract This paper presents the analysis of the kinematics, dynamics and controls of tendon-driven mechanism under the framework of signal flow graphs. For decades, the signal flow graphs have been applied in many areas, particularly in controls, for determining the closed-loop transfer function of a control system. The tendon-driven mechanism considered here consists of several subsystems including actuator-controller dynamics, mechanism kinematics and mechanism dynamics. Each subsystem will be derived and represented by signal flow graphs. The representation of the whole system can be carried out by connecting the graphs of subsystems at the corresponding nodes. Transfer functions can then be obtained by using Mason’s rules. A 3-DOF robot finger utilizing tendon-driven mechanism is used as an illustrative example.


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