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.

翻译：本文提出了一种用于四旋翼姿态控制的新型多模型预测控制（MMPC）方法，其性能接近非线性模型预测控制（NMPC），而计算效率与线性模型预测控制（LMPC）相当。传统的NMPC虽然有效，但计算量大，尤其对于需要高刷新率的姿态控制而言。相反，LMPC具有计算优势，但性能较差且仅具有局部稳定性。我们的方法基于姿态动力学的多个线性模型，每个模型配有一个线性模型预测控制器，并根据飞行条件在它们之间动态切换。我们利用间隙度量分析来最小化准确预测飞行器在各种条件下行为所需的模型数量，并引入软切换机制以确保控制器切换期间的系统稳定性。实验结果表明，仅需15个模型即可在各种设定点下精确控制飞行器姿态。与现有控制器（如增量非线性动态逆、滑模控制、LMPC和NMPC）的比较评估表明，我们的方法在效果上非常接近NMPC，优于其他方法，且运行时间与LMPC相当。