473 research outputs found

    An optimization-based approach to automated design

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    We propose a model-based, automated, bottom-up approach for design, which is applicable to various physical domains, but in this work we focus on the electrical domain. This bottom-up approach is based on a meta-topology in which each link is described by a universal component that can be instantiated as basic components (e.g., resistors, capacitors) or combinations of basic components via discrete switches. To address the combinatorial explosion often present in mixed-integer optimization problems, we present two algorithms. In the first algorithm, we convert the discrete switches into continuous switches that are physically realizable and formulate a parameter optimization problem that learns the component and switch parameters while inducing design sparsity through an L1L_1 regularization term. The second algorithm uses a genetic-like approach with selection and mutation steps guided by ranking of requirements costs, combined with continuous optimization for generating optimal parameters. We improve the time complexity of the optimization problem in both algorithms by reconstructing the model when components become redundant and by simplifying topologies through collapsing components and removing disconnected ones. To demonstrate the efficacy of these algorithms, we apply them to the design of various electrical circuits

    Beyond Simulation: Computer Aided Control System Design Using Equation-Based Object Oriented Modelling for the Next Decade

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    After 20 years since their birth, equation-oriented and object-oriented modelling techniques and tools are now mature, as far as solving simulation problems is concerned. Conversely, there is still much to be done in order to provide more direct support for the design of advanced, model-based control systems, starting from object-oriented plant models. Following a brief review of the current state of the art in this field, the paper presents some proposals for future developments: open model exchange formats, automatic model-order reduction techniques, automatic derivation of simplified transfer functions, automatic derivation of LFT models, automatic generation of inverse models for robotic systems, and support for nonlinear model predictive control

    Towards more Dependable Verification of Mixed-Signal Systems

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    The verification of complex mixed-signal systems is a challenge, especially considering the impact of parameter variations. Besides the established approaches like Monte-Carlo or Corner-Case simulation, a novel semi-symbolic approach emerged in recent years. In this approach, parameter variations and tolerances are maintained as symbolic ranges during numerical simulation runs by using affine arithmetic. Maintaining parameter variations and tolerances in a symbolic way significantly increases verification coverage. In the following we give a brief introduction and an overview of research on semi-symbolic simulation of both circuits and systems and discuss possible application for system level verification and optimization

    Numerical methods for accelerating transient simulation of dense parasitic RC networks

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