Robust Simultaneously Stabilizing Decoupling Output Feedback Controllers for Unstable Adversely Coupled Nano Air Vehicles

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 (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input-multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, 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 an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an 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 an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

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

“…Here, a brief description of the dynamic model for a single propeller fixed-wing NAV is provided, and more details can be found in [2], [1]. Generally, in a fixed-wing aircraft, the actuator ′ s bandwidth would be much higher than that of the plant, whereas it is not true in the case of a NAV.…”

“…Hence, a genetic algorithm based iterative approch refered as MERSE algorithm is developed to solve the problem given in (40). This algorithm has a population-based GA solver where GA employs the same steps of GA-SCP and GA-RSSD solver mentioned in [1] (these steps are also given in supporting material). Search Variables: The search variables of GA solver are the coefficients of W ei (s) and W eo (s).…”

“…They are widely used for intelligence operations, battlefield surveillance, reconnaissance, and disaster assessment missions. These small vehicles have severe dimensional and weight constraints as their overall dimensions and weights need to be lower than 75 mm and 20 g, respectively [1]. Figure 1(a) shows a typical fixed-wing NAV that weighs 19.4 g and has an overall dimension of 75 mm [2].…”

“…In this paper, for convenience, we use the terms plant and estimator to represent the plant model and estimator model, respectively. More details about these plant characteristics are given in [1], [2]. Due to these plant characteristics, the NAV's require complex feedback controllers to accomplish a mission.…”

Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and Inclusion of Agents and Switching Topologies

“…In [29], the central plant is obtained by solving a 2-block optimization problem. However, the central plant is identified in [30]- [31] using the maximum ν-gap metric of the systems that requires SS. The definition of this ν−gap metric-based central plant and the maximum ν-gap metric of the systems is given in Section II.…”