Uploaded January 2021 | Updated September 2026, 3 weeks ago
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.
Link to Survey Paper: arxiv.org/abs/2011.11104
Selected – but indicative – open-source MPC releases for MAVs
* github.com/ethz-asl/mav_control_rw
* github.com/uzh-rpg/rpg_mpc
* github.com/DentOpt/denmpc
* github.com/klaxalk/multirotor-control-board
Selected – but indicative – open-source MPC software packages with broader scope
* github.com/ethz-adrl/control-toolbox
* cvxgen.com/docs/index.html
* acado.sourceforge.net/doc/html/d4/d26/example_013.html
* yalmip.github.io
* mpt3.org
* do-mpc.com/en/latest
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.
Link to Survey Paper: arxiv.org/abs/2011.11104
Selected – but indicative – open-source MPC releases for MAVs
* github.com/ethz-asl/mav_control_rw
* github.com/uzh-rpg/rpg_mpc
* github.com/DentOpt/denmpc
* github.com/klaxalk/multirotor-control-board
Selected – but indicative – open-source MPC software packages with broader scope
* github.com/ethz-adrl/control-toolbox
* cvxgen.com/docs/index.html
* acado.sourceforge.net/doc/html/d4/d26/example_013.html
* yalmip.github.io
* mpt3.org
* do-mpc.com/en/latest
