Real-time numerical differentiation of sampled data using adaptive input and state estimation

Abstract

Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper proposes an algorithm for estimating the numerical derivative of a signal from noisy sampled data measurements. The method uses adaptive input estimation with adaptive state estimation (AIE/ASE), and thus it requires only minimal prior information about the signal and noise statistics. Furthermore, since the estimates of the derivative at step k provided by AIE/ASE depend only on data available up to step k, AIE/ASE is thus implementable in real time. The accuracy of AIE/ASE is compared numerically to several conventional numerical differentiation methods. Finally, AIE/ASE is applied to simulated vehicle position data, generated in the CarSim simulator software.

Keywords:

• Numerical differentiation
• Adaptive system
• Kalman filter
• Input estimation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by Ford Motor Company and NSF grant CMMI 2031333.