Differential Flatness

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Aerodynamic Prior-Free Coordinated Trajectory Generation and Tracking Control for a Tail-Sitter UAV
Sep 10, 2026Erchao Rong, Zihao Liu, Junning Liang +5Aerial Robotics UAV Navigation
School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China
Dispersive Forward Tree Search for Optimal Control: Coverage, Complexity, and Computation
Aug 26, 2026Shashank A. Deshpande, Jonathan P. HowDifferential Flatness Kinodynamic Motion Planning
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems
Aug 21, 2026Nishanth Bobbili, Pratyaksh Rao, Luca Morando +2Differential Flatness Robot Trajectory Tracking
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.
Flatness-Preserving Residual Learning for Real-Time Tight Quadrotor Formation Flight
Jul 14, 2026Pei-An Hsieh, Fengjun Yang, Nikolai Matni +1Multi-Robot Formation Control Residual Dynamics Learning
1GRASP Laboratory, University of Pennsylvania, PA, USA
Coordinated Incremental Trajectory Tracking of a Tailsitter Drone
Jul 13, 2026Evangelos Ntouros, Ewoud J. J. SmeurRobot Trajectory Tracking Aerial Robotics
All authors are with Faculty of Aerospace Engineering, Delft University of Technology, The Netherlands.
Exploiting Differential Flatness for Efficient Learning-based Model Predictive Control of Constrained Multi-Input Control Affine Systems
Apr 27, 2026Tobias A. Farger, Adam W. Hall, Angela P. SchoelligNonlinear MPC Model Predictive Control
Learning Systems and Robotics Lab at the Technical University of Munich, Munich, Germany. · Learning Systems and Robotics Lab at the University of Toronto Institute for Aerospace Studies (UTIAS) and the Vector Institute for Artificial Intelligence, Toronto, Canada
Ultrafast Sampling-based Kinodynamic Planning via Differential Flatness
Mar 17, 2026Thai Duong, Clayton W. Ramsey, Zachary Kingston +2Constrained Motion Planning Kinodynamic Motion Planning
Department of Computer Science, Rice University, Houston, TX, 77005, USA
Diff-Muscle: Efficient Learning for Musculoskeletal Robotic Table Tennis
Mar 9, 2026Wentao Zhao, Jun Guo, Kangyao Huang +2Robotic Table Tennis Differential Flatness
Department of Computer Science and Technology, Tsinghua University, China.

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Aerodynamic Prior-Free Coordinated Trajectory Generation and Tracking Control for a Tail-Sitter UAV

Erchao Rong, Zihao Liu, Junning Liang, Jianguo Wang+4

School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China

Aerial Robotics UAV Navigation Model Predictive Control UAV Trajectory Optimization Differential Flatness

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This paper presents a coordinated trajectory generation and tracking control framework for a tail-sitter unmanned aerial vehicle (UAV), which does not require aerodynamic priors identified for a specific airframe while addressing the challenge of flight control under highly nonlinear aerodynamics across the full flight envelope. The core innovation lies in employing phase-specific aerodynamic modeling strategies for planning and tracking, tailored to their distinct functional characteristics, without requiring airframe-specific aerodynamic priors. Specifically, the phi-theory model under coordinated flight is employed to derive an analytic differential flatness mapping, and a simplified but locally accurate model is established for predictive control to enable real-time aerodynamic parameter estimation. The proposed framework is evaluated extensively through both simulation and challenging real-world flight tests under mild wind conditions, showing high-precision tracking and adaptability across the tested aerodynamic conditions. To the best of our knowledge, this is the first real-world demonstration of accurate trajectory tracking over tested flight regimes spanning the full envelope of a tail-sitter UAV without relying on aerodynamic identification campaigns. The source code of our framework is available at: https://github.com/SYSU-HILAB/AP-PnC.