Performance-based control system design automation via evolutionary computing

Tan, Kiang Hwee; Li, Yun

doi:10.1016/s0952-1976(01)00023-9

Cited by 23 publications

(15 citation statements)

References 34 publications

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“…Compared with the working mechanism of a standard GA or an elitism GA, the Lamarckian mechanism of evolution is based on the theory that specific characteristics of an organism are directly inheritable by its offspring and survives the given environment [32,33]. In evolutionary algorithms, it has been shown that Lamarckian inheritance is an effective way to improve machine learning and convergence characteristics [34].…”

Section: Lamarckian Particle Filtermentioning

confidence: 99%

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Saldivar

Bai

et al. 2019

Energies

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Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

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Section: Lamarckian Particle Filtermentioning

confidence: 99%

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Saldivar

Bai

et al. 2019

Energies

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“…A number of automatically 'evolved' top-performing candidates will finally merge as optimal designs. Its unique search and adaptive learning power has facilitated design automation, transforming a manual iterative tuning process based on existing CAD or CACSD packages into CAutoCSD (Chong and Li 2002;Tan and Li 2001). The advantages of such CAutoCAD over traditional CACSD approaches include meeting multiple design objectives, offering design quality improved beyond the present performance bounds, and dramatically reducing design cycle and time-to-market.…”

Section: Evolution Enabled Cautocsdmentioning

confidence: 99%

“…The recent progress in evolutionary and soft computing techniques has enabled the replacement of the human trial-and-error based iterative process with a computer-automated one (Li et al 1995;Ng 1995;Tan 1997;Tan and Li 2001). More importantly, EC reshapes the way we think in designing and modelling engineering systems, and unleashes the uncharted potential of design engineering.…”

Section: Introductionmentioning

confidence: 99%

“…More importantly, EC reshapes the way we think in designing and modelling engineering systems, and unleashes the uncharted potential of design engineering. With the help of EC techniques, mixed or multi-criterion objectives and hence the designs of linear time-invariant (LTI) control systems might now be unified under one banner: "performance satisfaction" (Li et al 1995;Tan and Li 2001).…”

Section: Introductionmentioning

confidence: 99%

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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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“…Recent research work shows the capabilities of rule extraction from a trained network positions [30] [31] neurocomputing as a good decision support tool. Recently Evolutionary Computation (EC) has been successful as a powerful global optimisation tool due to the success in several problem domains [2][42] [43] [44] [45]. EC works by simulating evolution on a computer by iterative generation and alteration processes operating on a set of candidate solutions that forms a population.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Paradigms and Regression Trees in Decision Support Systems

Tran

Abraham

Jain

2006

Computational Intelligence and Its Applications

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Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this chapter, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.knowledge. The learning process then builds up the decision rules. The developed rules are further fine tuned depending upon the quality of the solution using a supervised learning process.

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Performance-based control system design automation via evolutionary computing

Cited by 23 publications

References 34 publications

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

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

Soft Computing Paradigms and Regression Trees in Decision Support Systems

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