Optimization of inverse model identification for multi-axial test rig control

Optimization of inverse model identification for multi-axial test rig control

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Laboratory testing of multi-axial fatigue situations improves repeatability and allows a time condensing of tests which can be carried out until click here component failure, compared to field testing.To achieve realistic and convincing durability results, precise load data reconstruction is necessary.Cross-talk and a high number of degrees of freedom negatively affect the control accuracy.

Therefore a multiple input/multiple output (MIMO) model of the system, capturing all inherent cross-couplings is identified.In a first step the model order is estimated based on the physical fundamentals of a one channel hydraulic-servo system.Subsequently, the structure of the MIMO model is optimized using correlation of the outputs, to increase control stability and reduce complexity redken shades eq 07m driftwood of the parameter optimization.

The identification process is successfully applied to the iterative control of a multi-axial suspension rig.The results show accurate control, with increased stability compared to control without structure optimization.