Probabilistic Planning for Predictive Condition Monitoring and Adaptation Within the Self-Optimizing Energy Management of an Autonomous Railway Vehicle

Klöpper, Benjamin; Sondermann-Wölke, Christoph; Romaus, Christoph

doi:10.20965/jrm.2012.p0005

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…Energy management can use behavior planning to proactively schedule future energy demands according to the fulfillment of the current task, which increase the dependability of the mechatronic system (cf. [82]). Table 3.2 shows the values for operation modes derived from the multiobjective optimization from the active suspension module of the RailCab system.…”

Section: Resultsmentioning

confidence: 99%

“…[80][81][82]), the main challenge is to map the partial function solutions onto actions within the framework of PDDL (Planning Domain Definition Language, cf. [53]).…”

Section: Prerequisites and Inputmentioning

confidence: 99%

“…The present and deterministic plan is examined for estimated variances in order to proactively generate conditional branches, with alternative plans for critical system conditions. A threshold specifies the maximum probability of state deviations which would results in a generation of conditional branches (See [82], cf. in particular also [82] and [80].…”

Section: Descriptionmentioning

confidence: 99%

“…A threshold specifies the maximum probability of state deviations which would results in a generation of conditional branches (See [82], cf. in particular also [82] and [80]. )…”

Section: Descriptionmentioning

confidence: 99%

“…[80,81]; for a concrete example of creating a stochastic model cf. [82]). As a result, it is now possible to use the just-in-case-planning to generate alternative plans for situations that could occur with high probability during operation.…”

Section: Descriptionmentioning

confidence: 99%

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Methods of Improving the Dependability of Self-optimizing Systems

Dorociak

Gausemeier

2014

Lecture Notes in Mechanical Engineering

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Abstract. Various methods have been developed in the Collaborative ResearchCenter 614 which can be used to improve the dependability of self-optimizing systems. These methods are presented in this chapter. They are sorted into two categories with regard to the development process of self-optimizing systems. On one hand, there are methods which can be applied during the Conceptual Design Phase. On the other hand, there are methods that are applicable during Design and Development.There are domain-spanning methods as well as methods that have been specifically developed for particular domains, e.g., software engineering or control engineering. The methods address different attributes of dependability, such as reliability, availability or safety.Each section is prefaced with a short overview of the classification of the described method regarding the corresponding domain(s), as well as its dependability attributes, to provide the reader with a brief outline of the methods' areas of application. Information about independently applicable methods or existing relationships and interactions with other methods or third-party literature is also provided.The development process for self-optimizing mechatronic systems which was introduced in Chap. 2 consists of two main phases: Conceptual Design and Design and Development. The main result of the Conceptual Design is the Principle Solution, which includes all information required for the concrete development during the second phase. Conceptual Design PhaseEven as early as during the specification of the Principle Solution, the dependability of self-optimizing mechatronic systems can be evaluated and improved by employing appropriate methods. The result is an improved Principle Solution, as such methods take the whole system into account before splitting it up into domain-specific tasks for the following development phase.

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Section: Resultsmentioning

confidence: 99%

“…[80][81][82]), the main challenge is to map the partial function solutions onto actions within the framework of PDDL (Planning Domain Definition Language, cf. [53]).…”

Section: Prerequisites and Inputmentioning

confidence: 99%

Section: Descriptionmentioning

confidence: 99%

“…A threshold specifies the maximum probability of state deviations which would results in a generation of conditional branches (See [82], cf. in particular also [82] and [80]. )…”

Section: Descriptionmentioning

confidence: 99%

Section: Descriptionmentioning

confidence: 99%

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