Design and evaluation of advanced intelligent flight controllers

Reinforcement learning based methods could be feasible of solving adaptive optimal control problems for nonlinear dynamical systems. This work presents a proof of concept for applying reinforcement learning based methods to robust and adaptive flight control tasks. A framework for designing and examining these methods is introduced by means of the open research civil aircraft model (RCAM) and optimality criteria. A state-of-the-art robust flight controller - the incremental nonlinear dynamic inversion (INDI) controller - serves as a reference controller. Two intelligent control methods are introduced and examined. The deep deterministic policy gradient (DDPG) controller is selected as a promising actor critic reinforcement learning method that currently gains much attraction in the field of robotics. In addition, an adaptive version of a proportional-integral-derivative (PID) controller, the PID neural network (PIDNN) controller, is selected as the second method. The results show that all controllers are able to control the aircraft model. Moreover, the PIDNN controller exhibits improved reference tracking if a good initial guess of its weights is available. In turn, the DDPG algorithm is able to control the nonlinear aircraft model while minimizing a multi-objective value function. This work provides insight into the usability of selected intelligent controllers as flight control functions as well as a comparison to state-of-the-art flight control functions

Design of Autoland Controller Functions with Multi-Objective Optimization

The application of multiobjective optimization to the design of longitudinal automatic landing control laws for a civil aircraft is discussed. The control laws consist of stability and command augmentation, speed / flight path tracking, glide slope guidance, and a flare function. Multiobjective optimization is used to synthesize the free parameters in these controller functions. Performance criteria are thereby computed from linear as well as nonlinear analysis. Robustness to uncertain and varying parameters is addressed via a multimodel approach, via robustness criteria, and via statistical criteria. For each controller function an optimization problem setup is defined. Starting with the inner loops, the synthesis is sequentiallv expanded with each of these setups, eventually leading to simultaneous optimization of all controller functions. In this way, dynamic interactions between controller components are accounted for and inner loops can be compromised so that these can be used in combination with different outer loop functions. This reduces controller complexity while providing good overall control system performance

Integration of Rigid and Aeroelastic Aircraft Models using the Residualised Model Method

This article describes a procedure for development of an integrated model of a flexible aircraft by combining available flight dynamics and aeroelastic models. Both types of models mostly complement each other. However, two overlaps are typically present that should be properly taken care of. In the first place, rigid aircraft flight dynamics models usually already take the deformation of the airframe quasi-statically into account in the aerodynamics computation. Second, aeroelastic models also contain so-called rigid-body modes that correspond with the flight dynamics degrees of freedom. The adopted solution is to leave the flight dynamics model unchanged, and to remove rigid modes and the quasi-static influence on the flight dynamics from the aeroelastic model. This article presents an extension to previously published work, introducing a method for properly handling of so-called unsteady aerodynamic lag states. As a spin-off result, it will be shown how the proposed procedure can be used to correct rigid body dynamics in an aeroelastic state space model

Flexible Aircraft Modeling for Flight Control Law Design and Analysis

This report consists of two parts. In the first part we will discuss a so-called bottom-up approach to integration of flight mechanics and aeroelastic aircraft models. In the second part we demonstrate a new method for flutter analysis, the µ-method, that that has been developed at NASA-Dryden. The flutter analysis is an example application of the model discussed in the first part. After a brief introduction we will derive the flexible aircraft equations of motion from first principles, emphasizing how rigid body and structural aircraft models fit together, and what key-assumptions we make. The aerodynamic model is the most important component. We will discuss what and how data from different modeling sources (flight mechanical / aeroelastic) is to be used in the equations of motion. Our modeling software is Dymola: due to its object-oriented nature it is very suitable for multidisciplinary modeling purposes. As an example, we will show the implementation of a generic flexible aircraft model, and present some nonlinear simulation results

Flyover Noise Measurements of a Spiraling Noise Abatement Approach Procedure

DLR is investigating alternative low-noise approach procedures. One such procedure involves approaching the airport at a considerably higher altitude compared with standard landing trajectories, followed by a spiraling descent (helix flight path) shortly before the runway threshold. In this way, high ground noise levels by approaching aircraft are dislocated away from the common approach path and concentrated in the area near the helix path, i. e. in direct vicinity of the airport. Ground noise levels along the entire flight path prior to the helix are significantly reduced. The effectiveness of this procedure, referred to as Helical Noise Abatement Procedure (HeNAP), has been quantified by means of computational simulation analyses. These analyses also focused on aspects such as increased fuel burn and the occurrence of multiple noise events below the helix. In June 2009 a new DLR autopilot especially capable of tracking curved flight path trajectories was flight tested. Three HeNAPs were included in the flight plan, as well as standard and steep landing approaches. In addition, dedicated fly-over noise measurements were organized, supported by RWTH Aachen University. Twelve ground microphones have been installed along the common approach path and the helical flight segment. Despite adverse wind conditions at the only available test day, the measured data confirm the predicted noise dislocation effects. High noise levels have been limited to observer locations around the helix. DLR noise prediction methods have been compared with the experimental data. Predicted trends and noise dislocation effects are in good agreement with the measurements whereas the absolute numerical values show discrepancies. The flight test was closely accompanied by a R&D member of DFS to study the impact of spiraling procedures on ATM integration and air traffic controller workload, e. g. increased interaction with the pilots. Obviously, a spiraling approach procedure would not be implemented into the existing air traffic scenario with its common approach paths and highly frequented airports. The operational and economic environment still need more detailed investigation. Helical approaches become more feasible for implementation at small, low-frequented regional airports or during night hours to avoid possible noise related curfews

Robustness Analysis Applied to Autopilot Design, Part 3: Physical Modeling of Aircraft for Automated LFT Generation Applied to the Research Civil Aircraft Model

In this contribution the automated generation of LFT-based parametric uncertainty descriptions from a generic nonlinear aircraft-dynamics model, as used for the GARTEUR RCAM Design Challenge on Robust Flight Control, is described. For this purpose an object-oriented, equation-based modeling approach using the modeling environment Dymola was applied. Using this technique allows the modeling of physical systems as physical objects and phenomena, which are connected according to their physical interactions. This modeling in form of equations (not assignments!), as required for automated LFT generation, is different from modeling via signal flows or input-output block diagrams, as traditionally used for controller modeling. All necessary components are taken from an aircraft object library developed for this purpose. Different representations of one component may be present to allowmodel building of different complexity or various functionalities. By automatic equation manipulation a symbolic model code is generated from the parameter instantiated equations of each object and from the equations derived from the interconnection structure.This code is the base for an automated generation of the LFT-based parametric uncertaintydescriptio

Design of Robust Autopilot Control Laws with Nonlinear Dynamic Inversion

The application on Nonlinear Dynamic Inversion for the design of a robust attitude controller for a civil aircraft is discussed. The main function of the controller is to improve the flying qualities, including stability, of the aircraft dynamics. For parameter synthesis multi-objective optimisation is used. The required robustness is achieved via a multi-model approach and local robustness criteria. In addition to the feedback gains, physical parameters in the inverse model that are considered uncertain in the design model, are used as synthesis parameters. The control laws are automatically genereted from a symbolic aircraft model in the modeling language Modelica. The design was used as a stability and command augmentation function in an automatic Fly-by-Wire landing system and was successfully flight tested