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arXiv · 2509.21210

Next-Generation Aerial Robots -- Omniorientational Strategies: Dynamic Modeling, Control, and Comparative Analysis

Ali Kafili Gavgani·Amin Talaeizadeh·Aria Alasty·Hossein Nejat Pishkenari·Esmaeil Najafi
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Abstract

Conventional multi-rotors are under-actuated systems, hindering them from independently controlling attitude from position. In this study, we present several distinct configurations that incorporate additional control inputs for manipulating the angles of the propeller axes. This addresses the mentioned limitations, making the systems "omniorientational". We comprehensively derived detailed dynamic models for all introduced configurations and validated by a methodology using Simscape Multibody simulations. Two controllers are designed: a sliding mode controller for robust handling of disturbances and a novel PID-based controller with gravity compensation integrating linear and non-linear allocators, designed for computational efficiency. A custom control allocation strategy is implemented to manage the input-non-affine nature of these systems, seeking to maximize battery life by minimizing the "Power Consumption Factor" defined in this study. Moreover, the controllers effectively managed harsh disturbances and uncertainties. Simulations compare and analyze the proposed configurations and controllers, majorly considering their power consumption. Furthermore, we conduct a qualitative comparison to evaluate the impact of different types of uncertainties on the control system, highlighting areas for potential model or hardware improvements. The analysis in this study provides a roadmap for future researchers to design omniorientational drones based on their design objectives, offering practical insights into configuration selection and controller design. This research aligns with the project SAC-1, one of the objectives of Sharif AgRoLab.

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Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari, Esmaeil Najafi. 2025-09-25. Next-Generation Aerial Robots -- Omniorientational Strategies: Dynamic Modeling, Control, and Comparative Analysis. https://arxiv.org/abs/2509.21210

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