Investigating Cross-Axis Sensitivity and Misalignment in an Angular Accelerometer Measurement Unit

Jatiningrum, Dyah; Paassen, Marinus van; Visser, Coen C. de; Chu, Q.P.

doi:10.2514/6.2017-1905

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…As the sensor measurements also contain external disturbances, the controller compensates for their influences, making it more robust. However, for this type of controller to work, time derivatives of the controlled variables are required, given by nonlinear state observers or a new class of inertial sensors as angular accelerometers [15,16].…”

Section: Introductionmentioning

confidence: 99%

Incremental Nonlinear Control Allocation for an Aircraft with Distributed Electric Propulsion

Heer

Visser

Hoogendoorn³

et al. 2023

AIAA SCITECH 2023 Forum

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In this paper, a new nonlinear control allocation method is presented for a distributed electric propulsion (DEP) aircraft. As the electric propellers can be used actively for control, in addition to the control surfaces, the DEP aircraft is over-actuated. This freedom in control effectors can be exploited with an appropriate control allocation method. All control effectors are, therefore, captured in the incremental nonlinear control allocation (INCA) method, which allows taking into account effector nonlinearities and interactions introduced by the propellers. The INCA method is based on a real-time updated Jacobian model of the control effectiveness, thereby solving an efficient linear control allocation problem. This paper reformulates the original INCA method to optimize the control allocation for minimal propeller power, resulting in more efficient flight. A model predictive control (MPC) controller is added as an actuator dynamics compensation method. This ensures that the commanded control inputs from the INCA controller are achieved. The new controller is compared to a standard incremental nonlinear dynamic inversion (INDI) controller with a translational and rotational loop. It is shown in simulation that by combining INCA with MPC, the tracking performance is improved and efficiency increased by 6.1%.

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Section: Introductionmentioning

confidence: 99%