# Robust simultaneous stabilization and decoupling of unstable adversely coupled uncertain resource constraints plants of a nano air vehicle

## TL;DR

This paper develops a robust output feedback controller for nano air vehicles that stabilizes, decouples, and ensures robustness for uncertain, unstable, and coupled plants, using a novel synthesis method and genetic algorithm.

## Contribution

It introduces a new synthesis method for a robust simultaneous stabilization and decoupling controller based on a central plant and genetic algorithms.

## Key findings

- Successfully stabilizes and decouples nano air vehicle plants.
- Validated through numerical and hardware-in-the-loop simulations.
- Demonstrates robustness and performance in uncertain conditions.

## Abstract

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as Robust Simultaneously Stabilizing Decoupling (RSSD) output feedback controller that achieves simultaneous stabilization, desired decoupling, robustness, and performance for a finite set of unstable multi-input-multi-output adversely coupled uncertain plants. To synthesize a RSSD output feedback controller, a new method that is based on a central plant is proposed in this paper. Given a finite set of plants for simultaneous stabilization, we considered a plant in this set that has the smallest maximum $v-$gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of a RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of a RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating a RSSD controller for a fixed-wing nano air vehicle. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.