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Constraint based approach for the steady-state simulation of complex systems: Application to ship control

机译:基于约束的复杂系统稳态仿真方法:在船舶控制中的应用

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When the steady states are largely predominant with respect to transitional phases, steady-state simulation seems sufficient to predict the behavior of a complex system. Over the past 20 years, different modeling languages and dedicated tools have been developed to improve steady state simulation. In this paper, focus is made on steady-state simulation for system control and design. A model combining an emission sub-model with a ship propulsion sub-model was implemented in a constraint programming (CP) approach. It will help to determine the efficiency (i.e. the ability to model and solve the problem) and complexity of implementation (i.e. difficulties encountered during the implementation) of this approach. First, requirements for the steady-state simulation of complex systems are defined. Then, CP approach is shown to be able to answer these issues through experiments. This approach is then compared to one of the main simulation languages: Modelica. Although the two approaches (i.e Modelica and CP) are able to reverse models, the study shows that the use of Modelica principles for steady-state simulation involves some crippling limitations, such as the non-management of under/over-constrained systems, or inequalities. This study also shows that the constraint programming approach permits to meet some needs for steady-state simulation not yet covered by current approaches.
机译:当稳态在过渡阶段占主导地位时,稳态仿真似乎足以预测复杂系统的行为。在过去的20年中,开发了各种建模语言和专用工具来改善稳态仿真。在本文中,重点是用于系统控制和设计的稳态仿真。通过约束编程(CP)方法实现了将排放子模型与船舶推进子模型相结合的模型。这将有助于确定这种方法的效率(即建模和解决问题的能力)和实施的复杂性(即在实施过程中遇到的困难)。首先,定义了复杂系统稳态仿真的要求。然后,证明了CP方法能够通过实验来回答这些问题。然后将该方法与一种主要的仿真语言进行比较:Modelica。尽管这两种方法(即Modelica和CP)都可以逆向模型,但研究表明,将Modelica原理用于稳态仿真涉及一些严重的局限性,例如对欠约束/过度约束系统的不管理,或者不平等。这项研究还表明,约束编程方法可以满足当前方法尚未涵盖的稳态仿真的某些需求。

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