A combined use of the adaptive inverse plant modeling and iterative learning control strategy for service load simulations

Service load simulation is a crucial step in the design and development cycle of automotive vehicles. The objective of these tests is to reproduce acquired road load data in lab environment typically on a vibration test rig. This research proposes an algorithm for load data replication that makes use of adaptive inverse plant modeling (AIPM) and iterative learning control (ILC) techniques. The validation of the proposed approach is done through simulation using the single-input single-output (SISO) linear and non-linear test rig models. It is shown that both the tracking accuracy and convergence behavior are improved compared to an existing state of the art time waveform replication (TWR) method. Moreover, the LDR approach utilizes a time domain iterative control law and requires minimal processing time in between trials to update the drive signals for reproducing the load data in contrast to existing approach.