  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.
