Multi-objective optimisation is a proven, well-known parameter tuning technique in control design. It is especially suited to solve complex, multi-disciplinary design problems. This paper describes a software environment, called MOPS (Multi-Objective Parameter Synthesis), which supports the control engineer in setting up his design problem as a properly formulated multi-objective optimisation task. To this end, MOPS offers a basic control system criteria library, a generic multi-model structure for multi-disciplinary problems and a generic multi-case structure for robust control law design, as well as visualisation tools for monitoring the design progress. Several additional features for dealing with a large amount of parameters and criteria, distributed computation for time consuming computations and the use of extemal simulation and analysis servers are also provided. MOPS also supports parameter estimation in identification problems and optimisation based design assessment for robustness. The user is provided with a clear application program interface and a graphical user interface both implemented in MATLAB. To solve the underlying optimisation problem different powerful optimisation procedures are available.
This paper presents an overview of the DLR activities on active load alleviation in the CleanSky Smart Fixed Wing Aircraft project. The investigations followed two main research directions: the multi-objective, multimodel, structured controller design for the feedback load alleviation part and the use of Doppler LIDAR technologies for gust/turbulence anticipation. On this latter topic, the prior work made in the AWIATOR European FP6 project constituted a reference in terms of demonstrations and the objective was not to repeat these previous investigations with a real sensor in flight test but to develop new ideas for the exploitation of the Doppler LIDAR measurements for gust alleviation purposes. Very fruitful exchanges between industry partners and research organizations took place during this project and all the work presented in this paper has been made using a generic long-range benchmark provided by Airbus on the basis of the XRF-1 model.
Abstract. Multi-objective parameter synthesis (MOPS) provides a systematic way for computational control law tuning by directly specifying and iterating bounds and demands on stability quality, control performance, and physical control realization constraints. The design can be based on linear or non-linear models and the controller structure can be chosen in a convenient way taking advantage of the design engineer's knowledge and prior expertise. Given design specifications can be transformed directly into the mathematical design criteria needed for performing the method. Variations in structured parameter sets and operating conditions are taken care of by a multi-model formulation. The multi-objective/multi-model parameter tuning is done in a goal-oriented way by applying parameter optimisation. In this contribution it is shown that the MOPS systematic can be used to develop robust flight control systems with good performance and passenger comfort. IntroductionThe RCAM benchmark problem addresses the design of an autopilot for the final approach of a transport-type aircraft. The designed controller is required to be robust with respect to variations in speed, weight, centre of gravity position (both vertical and horizontal), time delays, non-linearities, and engine failure. The method which is applied is an optimisation-based multi-objective/multimodel/multi-parameter design methodology called "Multi-Objective Parameter Synthesis" (MOPS). Because of the inherent freedom in specifying controller structures, this approach can be used in combination with any controller synthesis method, and because of the possibilities in specifying the design goals in a convenient multi-criteria manner this yields a systematic goal-oriented way to solve the control design tuning problem.Two different approaches are adopted for synthesising controller parameters. For the lateral controller, direct optimization of the parameters of an appropriate controller structure was applied. For the longitudinal controller an
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