English

Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following

Systems and Control 2025-04-10 v1 Machine Learning Robotics Systems and Control

Abstract

Optimal trajectories that minimize a user-defined cost function in dynamic systems require the solution of a two-point boundary value problem. The optimization process yields an optimal control sequence that depends on the initial conditions and system parameters. However, the optimal sequence may result in undesirable behavior if the system's initial conditions and parameters are erroneous. This work presents a data-driven fuzzy controller synthesis framework that is guided by a time-optimal trajectory for multicopter tracking problems. In particular, we consider an aggressive maneuver consisting of a mid-air flip and generate a time-optimal trajectory by numerically solving the two-point boundary value problem. A fuzzy controller consisting of a stabilizing controller near hover conditions and an autoregressive moving average (ARMA) controller, trained to mimic the time-optimal aggressive trajectory, is constructed using the Takagi-Sugeno fuzzy framework.

Keywords

Cite

@article{arxiv.2504.06500,
  title  = {Data-driven Fuzzy Control for Time-Optimal Aggressive Trajectory Following},
  author = {August Phelps and Juan Augusto Paredes Salazar and Ankit Goel},
  journal= {arXiv preprint arXiv:2504.06500},
  year   = {2025}
}

Comments

6 pages, 10 figures, submitted to MECC 2025

R2 v1 2026-06-28T22:51:42.376Z