Integrated Extremal Control and Explicit Guidance for Quadcopters
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Integrated Extremal Control and Explicit Guidance for Quadcopters Evan Kawamura1
· Dilmurat Azimov1
Received: 16 December 2019 / Accepted: 19 May 2020 © Springer Nature B.V. 2020
Abstract The research study aims to create a framework for autonomous control technology for unmanned aerial vehicles with realtime target-relative guidance capabilities, which leverages onboard decision-making to provide targeting and re-targeting solutions. Thus, this paper aims to develop extremal control and guidance functions in the context of the optimal control problem and their integration for applications. Solving the optimal control problem leads to a constant motor thrust case and trivial and nontrivial cases for the variable motor thrust case. As illustrative examples, two quadcopter maneuvers use integrated extremal control and explicit guidance. The first maneuver is the quadcopter taking off to the desired altitude using maximum and then intermediate thrust. The second maneuver has the quadcopter traveling to a waypoint over an agricultural field. The DJI Onboard Software Development Kit provides a method to implement the proposed integration of extremal control and explicit guidance onboard a Raspberry Pi connected to the DJI M100 quadcopter. Simulated and experimental flight tests demonstrate that the integration of extremal control and explicit guidance allows the DJI M100 to reach the desired locations and velocities for both maneuvers. Keywords Guidance · Optimal control · Trajectory · Unmanned aerial vehicle
1 Introduction The goal of the research study is to create a framework for integrating targeting, guidance, navigation, and control (TGNC) functions for implementation and applications. In this study, the first step towards a complete TGNC involves integrating control and guidance functions. The first subsection explores UAV control research, the second subsection considers nonlinear controllers, the third subsection discusses UAV experiments with optimal control, the fourth subsection discusses UAV guidance research, and the last subsection provides a brief overview of this paper’s work.
1.1 UAV Control Research Many research studies focus on the development of control technology of unmanned aerial vehicles (UAVs), including Evan Kawamura
[email protected] Dilmurat Azimov [email protected] 1
Mechanical Engineering, University of Hawaii: Manoa Holmes Hall, 2540 Dole Street, Honolulu, HI 96822, USA
some works focused on optimality. In particular, the H∞ synthesis using robust control theory performed better than LQR synthesis with optimal control theory for maintaining a specific position in blustery conditions. However, it is unknown which controller performs better for maneuvers such as flying to a waypoint [21]. Another study used taskoriented optimal control strategies to improve flight time by generating energy-optimal coordinated motions with a Model Predictive Control (MPC) strategy. Their MPC method finds two-dimensional optimal control policies only if the system model is accurate, and inaccurat
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