Enabling control software generation by using mechatronics modeling primitives

Foeken, M.J.; Cabrera, Alicia Triviño; Voskuijl, Mark; Tooren, M.J.L. Van

doi:10.1016/j.aei.2012.02.005

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…If yes, the process is stopped and the output is a final design for the detailed design stage; otherwise, the DEE will generate a new vehicle configuration or variant and perform an iteration again. There is an agent based module named Communication in Figure 3, which is represented by two feedback arrows from the Converger and Evaluator to the MMG and Initiator, respectively [6,7]. The Communication module not only exchanges the data and information between various DEE models, but also controls the overall process.…”

Section: Figure 3 Overview Of the Deementioning

confidence: 99%

Knowledge Based Engineering to support electric and electronic system design and automatic control software development

Tian

Voskuijl

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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The level of software integration in vehicles, such as electric cars and aircraft, is rapidly increasing. Due to the increasing complexity of the embedded control software, significant delays can occur in development programs or even errors can be present in the control software of the final product. In the development of the electric and electronic system (E/E system), the analysis and specification for the architecture of the logical system, technical system and software itself includes many repetitive (manual) processes. Those repetitive processes are time consuming and are prone to errors. This research proposes new methods and tools that allow the designer to take electronic components, including control software, into account already in the conceptual design stage of complex systems. These new methods are based upon the principles of Knowledge Based Engineering (KBE), which is essentially a combination of computer aided design (CAD) and artificial intelligence (AI). The proposed methods can establish the relationship from the logical system architecture to the technical system architecture and finally the software components. Moreover, the proposed tools can model the logical, technical architecture and automatically generate the software components. The software language GDL, which is particularly suitable for the representation of complex systems and the development of KBE applications, has been used to develop the tools. The development of an Anti-lock Braking System (ABS) for a novel electric vehicle configuration has been chosen as test case. Based on a single intelligent product model, that contains the main design parameters of the vehicle specified by the designer, two models are generated automatically; (1) the simulation model of the physical plant and the associated control system, and (2) the control software. For a specific vehicle configuration, the simulation model can be used to test the control system and to optimize the parameters of the control system. In the case of an ABS, a braking maneuver is simulated. Next, the software components are generated automatically. The simulation model is used to test the software components for a range of conditions. The results show that the of the software components are automatically updated when the physical plant of the E/E systems or top level overall design changes. The final source code is wellstructured and easy to understand due to the fact that there is a direct relation between the vehicle design parameters specified in the original product model and the variables and their values in the data model of the software components. The proposed design methods and tools can in principle be applied to any dynamic system with a high level of software integration, such as e.g. unmanned aerial vehicles.

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Section: Figure 3 Overview Of the Deementioning

confidence: 99%

Knowledge Based Engineering to support electric and electronic system design and automatic control software development

Tian

Voskuijl

2013

2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)

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“…Rocca [10] argues that the method should facilitate parametric modeling of complex products, investigation of multidisciplinary performances, and optimization in a reliable and time efficient manner. In a special issue of the journal of Advanced Engineering Informatics, there are six literatures which demonstrate the recent methodologies and techniques to build knowledge-based engineering systems [11][12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

A User-Oriented Design Knowledge Reuse Model

Hao

Yan

Wang

et al. 2013

ISRN Industrial Engineering

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To promote the level of reusing the accumulated design knowledge in the company, we intend to develop a knowledge reuse model in this paper. First, the design knowledge is discussed, and a unified design knowledge model is proposed. Following that, we identify three design knowledge reuse patterns which contribute to the understanding of how designers reuse knowledge during the design process. Based on that, we propose a design knowledge reuse model. This model can provide design knowledge according to the design knowledge reuse patterns. The model also provides the ability to update itself according to the captured actions of the knowledge users. At last, the method is validated with a research cooperator on the hydropneumatic spring component.

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“…Even though companies and researchers focused on industrial automation emphasize the need for increasing the integration and re-use of codes, algorithms, and other engineering artifacts, such needs are not met in existing simulations on the SCADA system level (i.e., Supervisory Control and Data Acquisition system level, which is explained in details later). Current mechatronic nature frequently, i.e., the systems are created by collaborative work of several engineering disciplines [2,3]. To properly support the design of simulations, it is needed to integrate heterogeneous data and tools from such different engineering areas.…”

Section: Introductionmentioning

confidence: 99%