Formulation of the Alpha Sliding Innovation Filter: A Robust Linear Estimation Strategy

Area of Science:

• Control Systems Engineering
• Signal Processing
• Estimation Theory

Background:

• The sliding innovation filter (SIF) is an estimation strategy utilizing measurement error as a switching hyperplane.
• SIF offers a robust and stable, albeit sub-optimal, estimation approach.
• Existing SIF methods can be further optimized for improved performance.

Purpose of the Study:

• To introduce a reformulated sliding innovation filter, termed the alpha sliding innovation filter (ASIF).
• To enhance the estimation performance and robustness of the SIF by incorporating a forgetting factor.
• To validate the ASIF's effectiveness across various dynamic systems.

Main Methods:

• Reformulation of the sliding innovation filter (SIF) by integrating a forgetting factor.
• Development and implementation of the alpha sliding innovation filter (ASIF).
• Experimental application and testing of the ASIF on a first-order thermometer, a second-order spring-mass-damper, and a third-order electrohydrostatic actuator (EHA).

Main Results:

• The ASIF demonstrates significantly improved estimation performance compared to the standard SIF.
• The filter maintains robustness against modeling uncertainties and external disturbances.
• Accurate state estimation was achieved across diverse system orders and complexities.

Conclusions:

• The alpha sliding innovation filter (ASIF) represents a significant advancement in estimation techniques.
• The inclusion of a forgetting factor is crucial for enhancing SIF performance.
• ASIF provides a reliable and accurate estimation solution for complex engineering systems.