Model-based controllers are at the heart of many robotic systems. For fixed-wing aircraft in particular, controllers are often driven by models of varying fidelity. Model fidelity selection is an application-specific design choice. On one end of the spectrum, a simple model will be computationally efficient, but at the risk of under modeling. On the other end of the spectrum, a complex model has the potential to better capture system dynamics, but at the risk of increased complexity and potential over modeling. A viable middle ground in model selection is choosing a simple model parametrized by a set of state-dependent constants. Constants can represent unknown or random model parameter to be identified, such as intrinsic system properties or noise terms. The approach taken here is to capture fixed-wing aircraft model uncertainty including noise terms due to wind effects with a set of parameters that can easily be estimated from training data. A technique based on weighted least squares, know as locally weighted models (LWMs), has shown promise in such applications. LWMs strike a balance between simplicity and complexity by representing dynamic systems with a family of locally linear models. To demonstrate the potential of this modeling technique for complex systems, an augmented aircraft model is presented as an example, with a procedure for identifying unknown parameters. The LWM’s predictive capability is compared against a standard non-parametric model as well as flight data collected on a small fixed-wing unmanned aerial vehicle.
- Dynamic Systems and Control Division
Online Locally Weighted Least Squares Parameter Identification for Fixed-Wing Aircraft
Basso, B, & Hedrick, JK. "Online Locally Weighted Least Squares Parameter Identification for Fixed-Wing Aircraft." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 3: Renewable Energy Systems; Robotics; Robust Control; Single Track Vehicle Dynamics and Control; Stochastic Models, Control and Algorithms in Robotics; Structure Dynamics and Smart Structures; Surgical Robotics; Tire and Suspension Systems Modeling; Vehicle Dynamics and Control; Vibration and Energy; Vibration Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 325-332. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8819
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