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Data-Driven Generalized Iterative Predictive Control with IESO for UAV Trajectory Tracking
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AI
DOI：10.1007/s12555-026-00073-y.png)
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Abstract
En 中文
In this article, a data-driven generalized iterative predictive control strategy is presented to address trajectory tracking challenges of unmanned aerial vehicle (UAV) operating in nonlinear, coupled, and disturbed environments. Within this framework, input-output (I/O) data are utilized to eliminate dependence on precise dynamic models and to enhance adaptability. A nominal dynamic linearization (DL) system is adopted to decouple pseudo-partial derivative (PPD) estimation from external disturbance. Disturbance effects are compensated through an iterative extended state observer (IESO). Computational efficiency comparable to the compact-form DL(CFDL) method is achieved. In addition, historical data, similar to those employed in partial-form and full-form DL methods, are incorporated to further improve control performance. Comparative simulations indicate significant improvements in tracking accuracy, convergence speed, and robustness over existing data-driven methods. The effectiveness of the proposed framework under dynamic conditions is demonstrated through experimental validation on the UAV system.
Keywords:
Data-driven
Iterative learning control (ILC )
Dynamic linearization (DL )
Predictive control
Unmanned aerial vehicle (UAV )
Iterative extended state observer (IESO)
Journal
IF:
2.9
Papers:
216
Citations:
6.5K