Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers

Abstract

Incremental nonlinear dynamic inversion (INDI) is a widely used approach to controlling UAVs with highly nonlinear dynamics. One key element of INDI-based controllers is the control allocation realizing pseudo controls using available actuators. However, the tracking of commanded pseudo controls is not the only objective considered during control allocation. Since the approach only works locally due to linearization and the solution is often ambiguous, additional aspects like control efforts or penalizing the deviation of certain states must be considered. Conducting the control allocation by solving a quadratic program this results in a considerable number of weighting parameters, which must be tuned during control design. Currently, this is conducted manually and is therefore time consuming. An automated approach for tuning these parameters is therefore highly beneficial. Thus, this paper presents and evaluates a model-based approach automatically tuning the control allocation parameters of a tiltrotor VTOL using an optimization algorithm. This optimization algorithm searches for optimal parameters minimizing a cost functional that reflects the design target. This cost functional is calculated based on a test mission for the VTOL which is conducted within a simulation environment. The test mission represents the common operating range of the VTOL. The simulation environment consists of an aircraft model as well as a model of the INDI-based controller which is dependent on the control allocation parameters. On this basis, model-based optimization is conducted and the optimal parameters are identified. Finally, successful real-world tests on a 4-degrees-of-freedom testbench using the identified parameters are presented. Since the control allocation parameters can significantly influence the aircraft’s stability, the 4-DOF testbench for the aircraft is required for rapid validation of the parameters at a minimum amount of risk.