Real World System Architecture Design Using Multi-criteria Optimization: A Case Study

Kudikala, Rajesh; Mills, Andrew R.; Fleming, Peter J.; Tanner, G.; Holt, Jonathan E.

doi:10.1007/978-3-319-01128-8_16

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Systems engineering is applicable to the optimization of the control architecture on a higher level. R Kudikala et al [26] used multicriteria optimization for system architecture design in the case of an aero engine health management (EHM) system. Due to the large and discontinuous design search space, there are many qualitative and quantitative criteria that must be considered.…”

Section: Systems Engineeringmentioning

confidence: 99%

Review of Selection Criteria for Sensor and Actuator Configurations Suitable for Internal Combustion Engines

Comissiong¹,

Steffen²

2018

SAE Technical Paper Series

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This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and nonminimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture.

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Section: Systems Engineeringmentioning

confidence: 99%

Review of Selection Criteria for Sensor and Actuator Configurations Suitable for Internal Combustion Engines

Comissiong¹,

Steffen²

2018

SAE Technical Paper Series

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“…Many researchers studied techniques for derivative design. These research efforts considered the whole set of design variables and designed the baseline and derivatives simultaneously [1][2][3][4][5][6][7][8][9][10]. Richard et al, Robert et al, and Deepak et al identified the important parameters for the family design by applying the market requirement analysis [1, 2, and 3]…”

Section: Introductionmentioning

confidence: 99%

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Park¹,

Chung²,

Lee³

2016

International Journal of Aeronautical and Space Sciences

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Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet new market demands while keeping the development time and cost to a minimum. Many researchers have studied the derivative design process, but these research efforts consider baseline and derivative designs together, while using the whole set of design variables. Therefore, an efficient process that can reduce cost and time for aircraft derivative design is needed. In this research, a more efficient design process is proposed which obtains global changes from local changes in aircraft design in order to develop aircraft derivatives efficiently. Sensitivity analysis was introduced to remove unnecessary design variables that have a low impact on the objective function. This prevented wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, uncertainty from the fidelity of analysis tools was considered in design optimization to increase the probability of optimization results. The Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were proposed to handle the uncertainty in aircraft conceptual design optimization. In this paper, Collaborative Optimization (CO) based framework with RBDO and PBDO was implemented to consider uncertainty. The proposed method was applied for civil jet aircraft derivative design that increases cruise range and the number of passengers. The proposed process provided deterministic design optimization, RBDO, and PBDO results for given requirements.

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Customer-oriented architecture refinement in multi-criteria synthesis of large-scale system architectures

Hambley

Mills

Kadirkamanathan

et al. 2017

2017 IEEE International Systems Engineering Symposium (ISSE)

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Real World System Architecture Design Using Multi-criteria Optimization: A Case Study

Cited by 4 publications

References 17 publications

Review of Selection Criteria for Sensor and Actuator Configurations Suitable for Internal Combustion Engines

Review of Selection Criteria for Sensor and Actuator Configurations Suitable for Internal Combustion Engines

Aircraft derivative design optimization considering global sensitivity and uncertainty of analysis models

Customer-oriented architecture refinement in multi-criteria synthesis of large-scale system architectures

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