Gap Reduced Minimum Error Robust Simultaneous Estimation For Unstable Nano Air Vehicle

TL;DR

This paper introduces a novel robust estimator, GRMERS, for unstable Nano Aerial Vehicles, combining gap reduction and minimum error principles to enable stable, simultaneous state estimation across multiple models.

Contribution

The paper develops the GRMERS estimator, integrating gap reducing compensators with a robust estimation framework, and formulates its design as a tractable optimization problem solved via genetic algorithms.

Findings

GRMERS provides reduced estimation errors across all flight conditions.

The estimator outperforms traditional H-infinity filters in robustness.

Simulation results validate the effectiveness of the proposed approach.

Abstract

This paper proposes a novel Gap Reduced Minimum Error Robust Simultaneous (GRMERS) estimator for resource-constrained Nano Aerial Vehicle (NAV) that enables a single estimator to provide simultaneous and robust estimation for a given N unstable and uncertain NAV plant models. The estimated full state feedback enables a stable flight for NAV. The GRMERS estimator is implemented utilizing a Minimum Error Robust Simultaneous (MERS) estimator and Gap Reducing (GR) compensators. The MERS estimator provides robust simultaneous estimation with minimal largest worst-case estimation error even in the presence of a bounded energy exogenous disturbance signal. The GR compensators reduce the gap between the graphs of N linear plant models to decrease the estimation error generated by the MERS estimator. A sufficient condition for the existence of a simultaneous estimator is established using LMIs and robust estimation theory. Further, MERS estimator and GR compensator design are formulated as non-convex tractable optimization problems and are solved using the population-based genetic algorithms. The performance of the GRMERS estimator consisting of MERS estimator and GR compensators from the population-based genetic algorithms is validated through simulation studies. The study results indicate that a single GRMERS estimator can produce state estimates with reduced errors for all flight conditions. The results indicate that the single GRMERS estimator is robust than the individually designed H inifinity filters.