Model Predictive Control for Micro Aerial Vehicles: A Survey
This survey helps MAV researchers and practitioners understand and select appropriate MPC strategies for multirotor control, offering guidance on design choices and recent trends.
This paper surveys model predictive control (MPC) strategies for Micro Aerial Vehicles (MAVs), particularly multirotors, organizing existing works by control law, constraint integration, fault tolerance, and application. It provides insights into selecting between linear and nonlinear schemes, tuning prediction horizons, and the benefits of disturbance observer-based tracking.
This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.