Identification and analysis of hydrostatic transmission system

The dynamic behavior of hydro-static transmission (HST) systems is studied experimentally and theoretically to formulate a linearized mathematical model of the HST system and a recursive identification model is then proposed. A computer-controlled test rig is developed and the system state responses are measured. The results obtained from the experimental work and the linearized models are used to build a recursive identification model in order to identify the system under study. Furthermore, comparisons among the experimental, simulated, and identified results are presented. These results show that recursive identification models are powerful tools that can be used for the identification and analysis of HST. Finally, parameter variations of the volume displacement and the motor torque are introduced to the system in order to study their effect on pressure and hydraulic motor speed.

[1]  Yassine Koubaa Recursive identification of induction motor parameters , 2004, Simul. Model. Pract. Theory.

[2]  Hyunsu Cho,et al.  A Parameter Sensitivity Analysis for the Dynamic Model of a Variable Displacement Axial Piston Pump , 1987 .

[3]  T.A. Tutunji DC Motor Identification using Impulse Response Data , 2005, EUROCON 2005 - The International Conference on "Computer as a Tool".

[4]  H. W. Wu,et al.  Influence of a relief valve on the performance of a pump/inverter controlled hydraulic motor system , 1996 .

[5]  İlyas Eker Open-loop and closed-loop experimental on-line identification of a three-mass electromechanical system , 2004 .

[6]  Noah D. Manring,et al.  Modeling and Designing a Hydrostatic Transmission With a Fixed-Displacement Motor , 1998 .

[7]  İlyas Eker,et al.  Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments , 2004 .

[8]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[9]  Mikael Norrlöf,et al.  Closed-Loop Identification of an Industrial Robot Containing Flexibilities , 2003 .

[10]  J W Richmond,et al.  The Quantification and Reduction of Piston Slap Noise , 1987 .

[11]  Mu-Tian Yan,et al.  Theory and application of a combined self-tuning adaptive control and cross-coupling control in a retrofit milling machine , 2005 .

[12]  Dierk Schröder,et al.  Online identification of a nonlinear mechatronic system , 2002 .

[13]  Michel Verhaegen,et al.  State-space system identification of robot manipulator dynamics , 1997 .