Multi-Model Predictive Attitude Control of Quadrotors

TL;DR

This paper presents a multi-model predictive control method for quadrotor attitude regulation that achieves near-NMPC performance with LMPC-like computational efficiency by dynamically switching among multiple linear models.

Contribution

It introduces a novel multi-model predictive control framework with gap metric analysis and soft switching, improving attitude control accuracy and stability while maintaining computational efficiency.

Findings

Achieves accurate attitude control with only 15 models.

Close performance to nonlinear MPC in various flight conditions.

Computational time comparable to linear MPC.

Abstract

This paper introduces a new multi-model predictive control (MMPC) method for quadrotor attitude control with performance nearly on par with nonlinear model predictive control (NMPC) and computational efficiency similar to linear model predictive control (LMPC). Conventional NMPC, while effective, is computationally intensive, especially for attitude control that needs a high refresh rate. Conversely, LMPC offers computational advantages but suffers from poor performance and local stability. Our approach relies on multiple linear models of attitude dynamics, each accompanied by a linear model predictive controller, dynamically switching between them given flight conditions. We leverage gap metric analysis to minimize the number of models required to accurately predict the vehicle behavior in various conditions and incorporate a soft switching mechanism to ensure system stability during controller transitions. Our results show that with just 15 models, the vehicle attitude can be accurately controlled across various set points. Comparative evaluations with existing controllers such as incremental nonlinear dynamic inversion, sliding mode control, LMPC, and NMPC reveal that our approach closely matches the effectiveness of NMPC, outperforming other methods, with a running time comparable to LMPC.