Adopting a Minimum Commitment Principle for Computer Aided Geometric Design Systems

Guan, Xiaohong; MacCallum, K. J.

doi:10.1007/978-94-009-0279-4_33

Cited by 11 publications

(7 citation statements)

References 6 publications

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“…These design specifications, treated as the design objectives in MO optimization, can be easily set via the "Objective Setup" GUI in the toolbox. Although determination of the priority set-tings in Table 10.1 may be a subjective matter and depends on the performance requirements, ranking the priorities is only optional and can be ignored for a "minimum-commitment" design (Guan and MacCallum 1996). If, however, an engineer commits to prioritizing the objectives, it is a much easier task than preweighting the different design specifications as required by conventional function aggregation approaches.…”

Section: Evolutionary Ultic Design Applicationmentioning

confidence: 99%

Multiobjective Evolutionary Algorithms and Applications

Tan¹,

Lee²,

k-c-tan³

et al. 2005

Advanced Information and Knowledge Processing

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Section: Evolutionary Ultic Design Applicationmentioning

confidence: 99%

Multiobjective Evolutionary Algorithms and Applications

Tan¹,

Lee²,

k-c-tan³

et al. 2005

Advanced Information and Knowledge Processing

View full text Add to dashboard Cite

“…They can be in the form of an overall composite objective or cost function, as commonly adopted by control engineers. They can also, preferably, be in the form of multiple independent criteria , if a 'least commitment' principle is to be adopted at an early stage of design (Guan and MacCallum 1996). Thus, for a given application, a control system can be automatically designed or invented if a search, machine learning or optimisation algorithm can accommodate these objectives under practical constraints.…”

Section: Performance Metrics For Design Unificationmentioning

confidence: 99%

“…9. Although determination of the objectives and the priorities vector may be a subjective matter and depends on the performances requirement, ranking the priorities may be unnecessary and can be ignored for a 'minimum-commitment' design (Guan and MacCallum 1996). If, however, the engineer commits himself to prioritising the objectives, it is a much easier task than weighting the objectives, which is somewhat guesswork in conventional optimisation.…”

Section: Multi-objective Scheme For Robust Controlmentioning

confidence: 99%

CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

Ang

Chong

et al. 2004

Int J Automat Comput

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This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible , analytically and practically , and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process.

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“…Having fulfilled these requirements, the system should also satisfy some time domain specifications as defined by the transient and steady-state responses. Although determination of the objective and the priority settings may be a subjective matter and depends on the performance requirements, ranking the priorities may be unnecessary and can be ignored for a 'minimum-commitment' design 19 . If, however, an engineer commits himself to prioritizing the objectives, it is a much easier task than weighting the different objectives that are compulsory in objective function aggregation approaches 6 .…”

Section: An Evolutionary Ultic Design Applicationmentioning

confidence: 99%

Automating Control System Design via a Multiobjective Evolutionary Algorithm

Tan¹,

Li²

2004

Applications of Multi-Objective Evolutionary Algorithms

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This chapter presents a performance-prioritized computer aided control system design (CACSD) methodology using a multi-objective evolutionary algorithm. The evolutionary CACSD approach unifies different control laws in both the time and frequency domains based upon performance satisfactions, without the need of aggregating different design criteria into a compromise function. It is shown that control engineers' expertise as well as settings on goal or priority for different preference on each performance requirement can be easily included and modified on-line according to the evolving trade-offs, which makes the controller design interactive, transparent and simple for real-time implementation. Advantages of the evolutionary CACSD methodology are illustrated upon a non-minimal phase plant control system, which offer a set of low-order Pareto optimal controllers satisfying all the conflicting performance requirements in the face of system constraints.

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Adopting a Minimum Commitment Principle for Computer Aided Geometric Design Systems

Cited by 11 publications

References 6 publications

Multiobjective Evolutionary Algorithms and Applications

Multiobjective Evolutionary Algorithms and Applications

CAutoCSD-evolutionary search and optimisation enabled computer automated control system design

Automating Control System Design via a Multiobjective Evolutionary Algorithm

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