Hierarchical model-based predictive controller for a hybrid UAV powertrain. (October 2021)

• Record Type: Journal Article
• Authors: Aksland, Christopher T.; Alleyne, Andrew G.
• Is Part Of: Control engineering practice. Volume 115(2021)
• Publication Date: 2021-10
• Subjects: Hybrid powertrain -- Model predictive control -- Hierarchical control -- Experimental validation; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104883
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: The emerging trend of vehicle electrification is transforming the transportation industry by replacing traditional mechanical and hydraulic components with higher performing, more reliable, and more efficient electrical components. However, the introduction of a complex electrical network onboard mobile systems poses significant challenges for control design. A notable challenge is the coordination of multi-domain and multi-timescale system dynamics. This article seeks to address this challenge through the design and validation of a model predictive controller for a hybrid unmanned aerial vehicle powertrain. A multi-domain extension of the graph-based modeling framework is formulated and used to model the multi-physics behavior of the air vehicle. An extensive model validation procedure is performed and the validated graph model is used to develop two control strategies: one baseline and one predictive controller. To coordinate multi-timescale system dynamics, the predictive controller leverages a hierarchical control architecture to plan a battery state of charge bound. The control strategies are experimentally validated and show that the advanced controller yields improvements in performance and reliability metrics while reducing fuel consumption by ∼ 10 % .