Sensing parameter selection for ultra-low-power system identification

In micro-scale electromechanical systems, power to perform accurate position sensing often greatly exceed the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on sampling rate, while energy dependence is driven by error that may be tolerated in final identified parameters.