Quality function deployment: A literature review

Theory of multiobjective optimization

Modelica is an object-oriented language for modeling of large, complex and heterogeneous physical systems. It is suited for multi-domain modeling, e.g., for modeling of mechatronics including cars, aircraft and industrial robots which typically consist of mechanical, electrical and hydraulic subsystems as well as control systems. General equations are used for modeling of the physical phenomena. No particular variable needs to be solved manually, the Modelica tools have enough information to do that automatically. The language has been designed to allow tools to generate efficient code automatically. The modeling effort is thus reduced considerably since model components can be reused and tedious and error-prone manual manipulations are not needed. The principles of object-oriented modeling and the details of the Modelica language as well as several examples are presented.

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Modelica-a language for physical system modeling, visualization and interaction

Linear parameter varying systems: A geometric theory and applications

Purpose – The author aims to review the fundamental concept of quality function deployment and to discuss the facts that the road to success for new product development is the identification of customers' requirements and their conversion into engineering design requirements. Thereafter, the author seeks to present an in‐depth review of the subject and to study five new cases on the topic of quality function deployment.Design/methodology/approach – The paper discusses the key elements of quality function deployment and the fact that the vision for the development of a comprehensive quality system can be built on the principles of quality function deployment taking customer requirements into consideration and relating them to design requirements.Findings – To make the product development task successful and bring competitive advantages to the core business, management must be committed to the needs of customers through marketing surveys and implementing these in the process of product development by conver...

Quality function deployment and its extensions

A methodology for multi-objective design assessment and flight control synthesis tuning

The design of robust attitude control laws for a civil aircraft with nonlinear dynamic inversion and multiobjective optimization is discussed. Dynamic inversion is a methodology that achieves linearization and decoupled command responses of the closedloop system via inverse model equations in the feedback loop. Using a linear outer loop controller the desired dynamic behavior is imposed. For tuning the free controller parameters, multi-objective optimization is used. The required robustness is achieved via a multi-model approach, as well as local robustness measures (e.g. gain and phase margins) as optimization criteria. As a new approach, not only the linear controller gains are optimized, but also physical parameters in the inverse model that are considered uncertain in the design model. The resulting control laws are used as inner loops of an autoland system, which was assessed for JAR-AWO requirements and successfully ∞ight tested.

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Design of Robust Dynamic Inversion Control Laws using Multi-Objective Optimization

https://elib.dlr.de/48985/1/sdarticle2.pdf

Aircraft wake vortex scenarios simulation package - WakeScene

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 a stability and command augmentation, a speed/flight path tracking, a glide-slope guidance, and a flare function. Mulitobjective 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 linear robustness criteria and via statisticle criteria computed from online Monte Carlo analysis. For each controller function, an optimization problem setup is defined. Starting with the inner loops, the synthesis is sequentially 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 such that these can be used in combination with different outer loop functions. This reduces controller complexity while providing good overall control system performance.

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Design of Autoland Controller Functions with Multiobjective Optimization

This paper presents a combined feedback and feedforward active load alleviation system and its associated design and tuning methodology The feedback part is strongly structured and its robust performance across the flight envelope is ensured by the use of a multi-model and multi-objective controller design approach The feedforward function is based on a Doppler LIDAR sensor and the processing of the measurements as well as their physical interpretation combines various ideas from the system identification, the signal processing, and the control design domains The proposed solution remains very easy to use and tune, thanks to a limited number of parameters that can easily be interpreted physically and that exhibit only very little and very predictable couplings The performance and behavior of the active load alleviation functions is shown extensively based on a representative flexible long range aircraft model (based on the Airbus XRF1 configuration)

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Hans-Dieter Joos

Papers

A methodology for multi-objective design assessment and flight control synthesis tuning

Design of Robust Dynamic Inversion Control Laws using Multi-Objective Optimization

Aircraft wake vortex scenarios simulation package - WakeScene

Design of Autoland Controller Functions with Multiobjective Optimization

Combined Feedback and LIDAR-Based Feedforward Active Load Alleviation