Integrated coevolutionary synthesis of mechatronic systems using bond graphs

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@PhdThesis{Jiachuan_Wang:thesis,
  author =       "Jiachuan Wang",
  title =        "Integrated coevolutionary synthesis of mechatronic
                 systems using bond graphs",
  school =       "University of Massachusetts - Amherst",
  year =         "2004",
  address =      "USA",
  month =        jan # " 1",
  keywords =     "genetic algorithms, genetic programming. Industrial
                 engineering, Mechanical engineering, Computer science",
  URL =          "http://scholarworks.umass.edu/dissertations/AAI3152758/",
  URL =          "http://search.proquest.com/docview/305175569",
  size =         "178 pages",
  abstract =     "Mechatronics is a natural stage in the evolution of
                 modern products, many containing components from
                 different engineering domains, such as mechanical,
                 electrical, and software control systems. As part of
                 concurrent engineering practise, mechatronics is a
                 synergistic system design philosophy to optimise the
                 system as a whole simultaneously. Yet there is still
                 lack of support of this design principle in practice.
                 To date, conventional design tools have been limited to
                 single domain problems and require a trial-and-error
                 synthesis process. In order to support the concurrent
                 synthesis process of mechatronic products, theoretical
                 modeling of multi-domain engineering systems, with a
                 formal unified representation and a well-defined
                 algorithmic and flexible synthesis procedure, is
                 needed. These are essential to accommodate the
                 complexity of such systems and support the design
                 automation process. In this work, multi-domain
                 mechatronic system design is treated as a network
                 synthesis problem, extending from single domain
                 electrical network synthesis. Desired design
                 performance is specified in an impedance matrix that
                 captures the dynamic relations of effort and flow
                 variables at input-output interaction ports. An
                 extended multi-port bond graph representation is
                 developed to unify power and signal flows at a
                 high-level abstraction across engineering domains,
                 which also integrates active control system design. The
                 unified representation of both physical systems and
                 their control systems in bond graphs is achieved by
                 applying {"}controller design in the physical domain{"}
                 philosophy, to design and synthesise the whole system
                 simultaneously at the conceptual design stage. The
                 graphical structure of bond graphs being close to
                 reality also gives intuitive physical insight of the
                 interactions among physical components for detailed
                 level design realisation and simulation in different
                 domains to verify the entire system. This approach
                 makes full use of computational power to automatically
                 explore the design space for both design configuration
                 and parametrisation using biology-inspired optimisation
                 techniques: genetic algorithms, genetic programming,
                 and coevolution. Bond graph elements are encoded as
                 genetic programming functional and terminal primitives,
                 to evolve low-level building blocks to high-level
                 functionality by applying genetic operations based on
                 population-based natural evolution. It aids design
                 exploration of a wider range of possible creative
                 design options and achieves synergy in coevolving
                 different subsystems, including both active control
                 strategies and physical system design configurations,
                 for overall system optimality. Two mechatronic design
                 case studies are provided: a one-axis robotic
                 manipulator system and a quarter-car suspension system.
                 The computational results are compared with the design
                 solutions obtained by human designers from
                 trial-and-error synthesis and theoretical analysis. The
                 coevolutionary synthesis approach is capable of
                 discovering design options with better performance,
                 more creativity and flexibility than those perceived by
                 human designers. It is our belief that the
                 establishment of such mechanism will enhance the
                 capability of computers to automatically generate and
                 evaluate innovative and alternative solutions to
                 multi-domain dynamic systems and enable intelligent
                 assistance to engineering designers at the early stages
                 of system modelling and development for concurrent
                 engineering practices.",
  notes =        "supervisor Janis P. Terpenny UMI Microform 3152758",
}

Genetic Programming entries for Jiachuan Wang

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