1. Nonlinear Model Predictive Horizon for Optimal Trajectory Generation.
- Author
-
Al Younes, Younes and Barczyk, Martin
- Subjects
PREDICTION models ,CLOSED loop systems ,PSYCHOLOGICAL feedback ,PREDICTIVE control systems ,SYSTEM dynamics ,ALGORITHMS ,NONLINEAR systems - Abstract
This paper presents a trajectory generation method for a nonlinear system under closed-loop control (here a quadrotor drone) motivated by the Nonlinear Model Predictive Control (NMPC) method. Unlike NMPC, the proposed method employs a closed-loop system dynamics model within the optimization problem to efficiently generate reference trajectories in real time. We call this approach the Nonlinear Model Predictive Horizon (NMPH). The closed-loop model used within NMPH employs a feedback linearization control law design to decrease the nonconvexity of the optimization problem and thus achieve faster convergence. For robust trajectory planning in a dynamically changing environment, static and dynamic obstacle constraints are supported within the NMPH algorithm. Our algorithm is applied to a quadrotor system to generate optimal reference trajectories in 3D, and several simulation scenarios are provided to validate the features and evaluate the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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