Multi-Model Predictive Control Using PCA-Based Model Reduction for UAV Attitude Control

This research brings one of the significant amendments in Multi-model Predictive Control (MMPC) for controlling the attitude of an underactuated quadrotor unmanned aerial vehicle (UAV). It integrates the high-tech performance of non-linear model predictive control (NMPC) along with the lightweight computational efficiency of Linear Model Predictive Control (LMPC). Although NMPC produces high performance, its high computational cost makes it unfeasible for real-time applications, such as an attitude controller. LMPC is a good way of achieving this and gives reasonable performance in practice, however, it does not work well with nonlinear dynamics and has stability and precision issues. The MMPC methodology in this work addresses these issues using linear quadrotor attitude dynamics models, particularized by principal component analysis (PCA)-based model reduction resulting in fewer required LMPCs. To reduce the “chattering effect” commonly seen in multi-model approaches, we present an adaptive gain scheduling approach that improves the smoothness of control switching which helps improve stability and reduce actuator wear. This proposed MMPC achieves NMPC-like performance at computational costs similar to LMPC, outperforming other control techniques such as incremental nonlinear dynamic inversion, sliding mode control, and traditional LMPC and NMPC.