Input design and online system identification based on Poisson moment functions for system outputs with quantization noise

We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive least-squares (BCRLS) approach. The rationale is to achieve acceptable estimation results in the presence of white noise, caused by low-budget encoders with low resolution. The input design and parameter estimation approaches are assessed and compared, experimentally, resorting to measurements taken from a laboratory cart system.