The Need

Multirotor drones increasingly need to execute precise, dynamic trajectories while operating under strict computational, actuator, and safety constraints. Nonlinear model predictive control can provide strong tracking performance but often requires substantial onboard computation, creating challenges for high-frequency, real-time operation. Conventional linear controllers are computationally lighter but can lose accuracy as flight dynamics become more nonlinear or demanding. A practical need remains for predictive control that combines high-performance trajectory tracking, explicit constraint handling, and reliable real-time computation.

The Technology

OSU engineers and collaborators have developed a computationally efficient model predictive control framework for multirotor aircraft that captures important nonlinear flight behavior within a formulation suitable for linear optimization. The framework uses an analytically derived representation of the vehicle dynamics, eliminating the need to train the controller on large flight datasets. This approach enables real-time trajectory optimization while accounting for vehicle state and actuator constraints. Experimental quadrotor testing demonstrated competitive trajectory tracking compared with nonlinear MPC at substantially lower computational burden.

Commercial Applications

• Autonomous inspection and monitoring requiring precise flight near infrastructure
• Warehouse, logistics, and delivery drones operating with onboard computing constraints
• High-performance UAV platforms requiring dynamic trajectory tracking
• Autonomous navigation systems where predictable real-time control and constraint handling are important

Benefits/Advantages

• Reduced computational burden
• Strong tracking performance
• No training dataset required
• Constraint-aware control