Evolutionary <i>L</i>∞ identification and model reduction for robust control

Tan, Kiang Hwee; Li, Yun

doi:10.1243/0959651001540591

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…From the measured frequency response data of the physical system as shown in Fig. 12.1, a fourth-order model of the VCM actuator was obtained using the "worst-case" system identification technique in the frequency domain (Tan and Li 2000), G is) -4.4xl0-%^4.87xl0--(12.4) 5'(^'+1.45x10^^ + 1.1x10')* As shown by the dashed line in Fig. 12.1, frequency responses of the identified fourth-order model accurately matches the physical measured data up to the range of 10"^ rad/s, which has far exceeded the working frequency range of a practical VCM actuator.…”

Section: The Physical Hdd Modelmentioning

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: The Physical Hdd Modelmentioning

confidence: 99%

Multiobjective Evolutionary Algorithms and Applications

Tan¹,

Lee²,

k-c-tan³

et al. 2005

Advanced Information and Knowledge Processing

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“…Thirdly, the choice of may prevent robustness to the large data. Finally, most of the time, ∞ norm is used to the model reduction for robust control [6] with the restricted case where the model parameters number is limited. The norms are widely used in many domains [7,8].…”

Section: Introductionmentioning

confidence: 99%

MixedLpEstimators Variety for Model Order Reduction in Control Oriented System Identification

Corbier

Carmona²

2015

Mathematical Problems in Engineering

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A new family of MLE typeLpestimators for model order reduction in dynamical systems identification is presented in this paper. A family ofLpdistributions proposed in this work combinesLp2(1<p2<2) andLp1(0<p1<1) distributions which are quantified by four parameters. The main purpose is to show that these parameters add degrees of freedom (DOF) in the estimation criterion and reduce the estimated model complexity. Convergence consistency properties of the estimator are analysed and the model order reduction is established. Experimental results are presented and discussed on a real vibration complex dynamical system and pseudo-linear models are considered.

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Performance-based control system design automation via evolutionary computing

Tan

2001

Engineering Applications of Artificial Intelligence

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--This paper develops a parallel evolutionary algorithm based design unification of linear control systems in both the time and the frequency domains under performance satisfactions. A speedup of near-linear pipelinability is observed for the parallelism implemented on a network of transputers of Parsytec SuperCluster. The approach is capable of tackling practical constraints such as actuator saturation or transportation delays, and can be automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage. Intelligently guided by the evolutionary optimization, control engineers are able to obtain an optimal "off-the-computer" controller by feeding the developed CACSD system with plant I/O data and customer specifications, without the need of formulating a differentiable performance index or linearly parameterization.Validation results against linear and nonlinear physical plants are convincing, with excellent closedloop responses and good robustness in the presence of practical constraints and perturbations.2

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Evolutionary L∞ identification and model reduction for robust control

Cited by 4 publications

References 21 publications

Multiobjective Evolutionary Algorithms and Applications

Multiobjective Evolutionary Algorithms and Applications

MixedLpEstimators Variety for Model Order Reduction in Control Oriented System Identification

Performance-based control system design automation via evolutionary computing

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