DLR contributions to the REAL project
Since the late sixties more and more aircraft types are equipped with an autoland (automatic landing) system. Such a system allows an aircraft to land safely even under extremely poor visibility conditions, considerably increasing the aircraft's operability.
Autoland systems have to fulfil very high safety standards. These standards are described in the so-called Joint Aviation Regulations - All Weather Operations (JAR-AWO). The design of control laws for automatic landing is an elaborate task, since many varying parameters, such as aircraft loading, terrain and runway profiles, atmospheric conditions, winds and turbulence, noise on guidance signals etc., have to be taken into account (see the Figure below). In addition, the design models, especially early in the aircraft development program, may show considerable uncertainty. Usually, lots of manual design cycles and considerable trial and error are required to finally meet the certification criteria and to satisfy the monitoring pilots. Design model updates may require additional design cycles.
Autoland Design, the REAL Project
The objective of the project REAL (Robust and Efficient Autopilot control Laws design), funded by the Commission of the European Union, was to investigate how modern robust control design methods can be used to improve the efficiency of the autopilot control laws design process. As a most challenging benchmark application, control laws for automatic landing (autoland) were chosen.
The REAL-consortium consisted of DLR (Institutes of Robotics and Mechatronics and Flight Systems , Airbus (France, Germany), NLR and TU-Delft (The Netherlands) and ONERA (France). During the project, autoland controllers for two dissimilar aircraft were developed, compare the next Figure. The first, based on RealCAM, a civil aircraft model provided by Airbus, allowed design teams from ONERA and DLR to apply and refine their proposed approaches, especially to autoland-specific requirements and issues. The second aircraft involved DLR’s ATTAS (Advanced Technologies Testing Aircraft System), for which both design teams had to deliver autoland control laws in a very short period of time to prove the efficiency of their processes. The designs for ATTAS were then tested in flight.
The design process had to fulfil the following criteria:
The benchmark problems involved the design of control laws for the final approach (starting at ~1000 ft above the runway threshold) using the Instrument Landing System and for the flare manoeuvre until touchdown of the main wheels on the runway. Much uncertainty regarding aerodynamic model parameters, strong wind and turbulence, as well as environmental effects such as runway slope or irregular terrain in front of the runway (as illustrated in the first Figure) had to be taken into account.
The DLR design approach
DLR enhanced and applied its flight control law design process, as described here.
The proposed DLR flight control laws design process is based on the long-standing experience of the Control Design Engineering group in the field of object-oriented modelling and multi-objective optimisation. The overall process is depicted in the following Figure. The principal steps (left hand side), as applied to the autoland designs, will be explained in the following subsections. To support the process, software tools and methods are available or have been developed. These are depicted to the right.
The aircraft models for the two benchmark problems, including environmental effects, disturbances, etc. were implemented in Dymola using the in-house developed Flight Dynamics Library, based on the object-oriented modelling language Modelica , see below. The code for simulation as well as the code for trimming (i.e. computing equilibrium control deflections for a specific aircraft state) were generated from the Modelica models and distributed with the benchmark problem descriptions and software to all REAL project partners.
Controller structure synthesis
The first actual design step is the definition of the controller structure. The structure as adopted for the autoland benchmarks is depicted in the Figure below. Its functions have been grouped into three main loops: stability and command augmentation, path / speed tracking, and guidance (in the figure separated by colour backgrounds red, yellow, and green respectively).
The task of the inner loop is to improve the stability and tracking of attitude command variables. This part of the controller was designed with Nonlinear Dynamic Inversion (NDI), which includes inverted model equations in the control laws. For the two designs (RealCAM and RealATTAS), these inverted equations were fully automatically generated from the aircraft models in Modelica, avoiding any need for manual coding work.
The task of the path tracking loops is to make the aircraft follow flight path and speed references. Four modes were designed: for the approach phase the Total Energy Control System (TECS) was used for decoupled tracking of flight path angle and speed commands, and a classical PD control law was used for lateral flight path tracking. Shortly before landing, the flare law, based on the so-called “variable Tau” principle, takes over in order to reduce vertical speed to an acceptable level for touchdown. The thrust is reduced simultaneously using a retard function. Laterally, a classical align mode takes over from the lateral path tracking mode in order to align the aircraft with the runway centre line in case of cross wind, while keeping lateral deviation to a minimum.
The task of the guidance loop is to derive flight path references from guidance signals for the path tracking loops. For autoland these are localizer (LOC) and glide slope (GS) radio signals. In order to improve the estimation of metric deviations from the approach path, an altitude over threshold estimation was implemented.
In the Feedback Signal Synthesis block air data measurements are filtered complementary with inertial counterparts in order to reduce the noise level due to turbulence. Also the side-slip angle is estimated for use in the inner loops.
Multi-objective parameter optimisation
In the proposed design process, free parameters in the control laws are automatically tuned using multi-objective optimisation (MOPS ). In order to handle complex control laws consisting of multiple interacting functions, as is the case in autoland, a new tuning strategy was developed that starts with tuning a single function, but sequentially leads to the optimisation of all controller functions simultaneously. In the case of autoland, for each controller function (see Figure above) an optimisation sub-task was defined. This involved modelling of the detailed function architecture and selection of appropriate design criteria for tuning and compromising. Tuning then started with the inner loop functions. After a satisfactory result had been achieved, the next function (i.e. longitudinal or lateral path tracking) was added and another optimisation was started. The optimiser may still adjust inner loop parameters, but by retaining the inner loop design criteria as well, it is prevented from distorting inner loop performance in case other outer loop functions (i.e. flare or align) are connected. In the following steps glide slope and flare/align modes were added, eventually leading to simultaneous optimisation of all control law functions.
In the optimisation of glide slope and flare functions, nominal performance was addressed via criteria from a single landing simulation. In the proposed design process, each type of uncertainty and varying parameters may be handled in the most natural way. Known varying parameters are automatically addressed in the Dynamic Inversion-based control laws. Uncertainty of model parameters is addressed by multi-case optimisation, i.e. by compromise tuning for nominal and worst-case parameter combinations simultaneously.Unspecified uncertainty (e.g. unmodelled dynamics, time delays) is addressed by imposing sufficient stability margins via design criteria. As a new contribution, unknown varying disturbance parameters were addressed as in prescribed assessment of autoland control laws, namely by incorporating risk criteria computed from on-line Monte-Carlo analysis in the optimisation. In this way an acceptable solution was found automatically, whereas otherwise fulfilling risk requirements turned out to be difficult to achieve.
In the design process, the purpose of assessment is to detect hidden weaknesses in the designed flight control system. If performance or robustness is unsatisfactory at some point, optimisation criteria, considered model cases or even the controller structure may have to be adapted. In the REAL designs, the following means for assessment were used:
After the RealCAM design was finished, the controller structure was tuned for ATTAS using the developed optimisation set-ups in only a few weeks, demonstrating a very short design cycle for a dissimilar aircraft. The ATTAS design was successfully flight tested during six automatic landings, see the next Figure, and the movie on the ATTAS page .
Both the RealCAM and ATTAS designs fulfilled practically all performance and robustness requirements, based on JAR-AWO specifications. Industrial evaluators found that the proposed design process fulfilled all requirements listed at the top of this page.