Model Predictive Control in Unmanned Aerial Vehicles
Summary
Model predictive control (MPC) has emerged as a pivotal strategy for the guidance and navigation of unmanned aerial vehicles (UAVs), marrying optimisation theory with real-time control. By solving a finite-horizon control problem at each sampling instant, MPC generates control inputs that satisfy system dynamics, actuator limits and environmental constraints. This receding-horizon approach enables explicit handling of multivariable coupling, nonlinear dynamics and state or input constraints. In practice, MPC frameworks range from linear predictive schemes for simple fixed-wing platforms to fully nonlinear model predictive control (NMPC) architectures for multirotor and hexacopter systems. Key advantages include superior trajectory-tracking accuracy, explicit safety guarantees via constraint enforcement and inherent robustness to disturbances when combined with state estimation. Applications span precision agriculture, inspection and infrastructure monitoring, search-and-rescue missions in cluttered environments and autonomous package delivery, highlighting the global impact of predictive controllers in improving UAV reliability and performance.
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Model Predictive Control in Unmanned Aerial Vehicles publication trend
The graph below shows the total number of articles in model predictive control in unmanned aerial vehicles across all publications each year (not limited to Nature Index journals).
Technical terms
Model predictive control (MPC): A feedback strategy that computes control actions by solving a constrained optimisation problem over a finite future horizon.
Receding horizon: The principle of repeatedly shifting the optimisation window forward in time, using only the first control input before resolving at the next step.
Nonlinear model predictive control (NMPC): An extension of MPC that directly incorporates nonlinear state equations and constraints in the optimisation formulation.
Moving horizon estimation (MHE): A real-time state and disturbance estimation method that solves an optimisation problem over a sliding window of past measurements.
Collision avoidance constraint: A mathematical condition embedded in the control optimisation to ensure safe separation from obstacles or no-fly zones.
References
- Linear and Nonlinear Controllers Applied to Fixed-Wing UAV. International Journal of Advanced Robotic Systems (2013).
- Nonlinear Model Predictive Control for Unmanned Aerial Vehicles †. Aerospace (2017).
- External force estimation and disturbance rejection for Micro Aerial Vehicles. Expert Systems with Applications (2022).
- System Identification and Nonlinear Model Predictive Control with Collision Avoidance Applied in Hexacopters UAVs. Sensors (2022).
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