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 addresses the problem of numerical differentiation for real-time implementation with minimal prior information about the signal and noise using adaptive input and state estimation. Adaptive input estimation with adaptive state estimation (AIE/ASE) is based on retrospective cost input estimation, while adaptive state estimation is based on an adaptive Kalman filter in which the input-estimation error covariance and the measurement-noise covariance are updated online. 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 from CarSim.

This paper is under review at the International Journal of Control

References in corpus (1)

• Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy

Cited by in corpus (2)

• Real-Time Kinematics-Based Sensor-Fault Detection for Autonomous Vehicles Using Single and Double Transport with Adaptive Numerical Differentiation
• System Identification and Adaptive Input Estimation on the Jaiabot Micro Autonomous Underwater Vehicle