NMPC-based Unified Posture Manipulation and Thrust Vectoring for Fault Recovery

TL;DR

The study addresses fault recovery for quadrotor-like platforms facing actuator failures by unifying posture manipulation with thrust vectoring on the morphing M4 platform. It develops a high-fidelity Euler–Lagrange model and a reduced-order prediction model (ROM) and implements a nonlinear model predictive controller (NMPC) that enables fault-tolerant flight without switching controllers. Simulations in Simscape demonstrate rapid stabilization and robust trajectory tracking after thruster failures, with accurate ROM predictions matching the full model. The work highlights how morphing capability and integrated control can extend altitude maintenance and safe landing under severe faults, informing practical fault-tolerant UAV designs.

Abstract

Multi-rotors face significant risks, as actuator failures at high altitudes can easily result in a crash and the robot's destruction. Therefore, rapid fault recovery in the event of an actuator failure is necessary for the fault-tolerant and safe operation of unmanned aerial robots. In this work, we present a fault recovery approach based on the unification of posture manipulation and thrust vectoring. The key contributions of this work are: 1) Derivation of two flight dynamics models (high-fidelity and reduced-order) that capture posture control and thrust vectoring. 2) Design of a controller based on Nonlinear Model Predictive Control (NMPC) and demonstration of fault recovery in simulation using a high-fidelity model of the Multi-Modal Mobility Morphobot (M4) in Simscape.

Figures (9)

• Figure 1: Multi-Modal Mobility Morphobot (M4), a versatile transforming robot capable of changing modes into rover (UGV) mode for wheeled ground locomotion, quadrotor (UAS) mode for aerial locomotion, and other modes depending on environmental challenges and energy efficiency demands.
• Figure 2: Free-body Diagram of M4, showing the main body and one leg. Each leg has two hip joints (frontal and sagittal), wheel joint which is driven by a motor, and a thruster that generates thrust force and moment for flight.
• Figure 3: Plot showing simulated states between the NMPC prediction model states and Simscape model during thruster failure event. The predicted model closely resembles the full fidelity model.
• Figure 4: Plots showing the center of mass trajectory and Euler angles of the robot during the failure event until it reached a stable recovery state. The failure timing is shown with the red circle and dashed line.
• Figure 5: Plot showing the simulated body linear and angular velocities from the beginning with fully functional thrusters, thruster failure event, and recovery states. The yaw is unstable due to the loss of a thuster and the NMPC prioritizing roll and yaw stability during the failure recovery phase.