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

2025-04-09

AI Summary

To address trajectory tracking instability in time-optimal multirotor UAV control arising from initial condition mismatches and parametric uncertainties, this paper proposes a data-driven hierarchical fuzzy control framework. Methodologically, it introduces the first integration of Takagi–Sugeno (T-S) fuzzy logic with ARMA-based dynamic modeling to jointly stabilize hover and enable aggressive maneuvers—such as aerial flips—while generating time-optimal trajectories via numerical solution of a two-point boundary value problem. The primary contribution is a novel fuzzy control paradigm that simultaneously ensures robustness and optimality, significantly enhancing tolerance to model discrepancies. Simulation and real-world flight experiments demonstrate that the proposed approach reduces convergence error by 42% during flip trajectory tracking and exhibits markedly superior resilience to initial state deviations compared to conventional optimal control methods.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Solves time-optimal trajectory tracking for multicopters
• Addresses undesirable behavior from erroneous initial conditions
• Combines fuzzy and ARMA control for aggressive maneuvers

Innovation

• Data-driven fuzzy control framework
• Time-optimal trajectory optimization
• Takagi-Sugeno fuzzy ARMA controller