A data-driven bilinear predictive controller design based on subspace method

Yang, Hua; Li, Ning; Li, Shaoyuan

doi:10.1002/asjc.306

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…The control signal is put from 0 to 30. In order to verify the validity of the proposed method, the SVD‐KL method and RBF Neural Networks method are presented for comparison. The simulation results are shown in Figs .…”

Section: Simulation Studymentioning

confidence: 99%

“…Nevertheless, issues of MPC performance of DPSs have appeared in recent years because of its complicated nature. Yang Hua and Li Ning gave a new data‐driven MPC based on bilinear subspace identification . Wei Wu and San‐Yin Ding proposed the concept of the space neural network to be applied in one‐step‐ahead neural predictive control .…”

Section: Introductionmentioning

confidence: 99%

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Model Predictive Control for Nonlinear Distributed Parameter Systems based on LS‐SVM

Ai¹,

Ye²

2013

Asian Journal of Control

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This paper presents a novel model predictive control configuration for nonlinear distributed parameter systems based on least squares support vector machine. First, a data‐based modeling methodology for an unknown nonlinear distributed parameter system is introduced. Subsequently, the model predictive control framework based on the aforementioned model is presented at the measured point. To verify the effectiveness and feasibility of the control arithmetic, simulation results from a tubular reactor with the controller designed by the presented procedure are given.

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Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Predictive Control for Nonlinear Distributed Parameter Systems based on LS‐SVM

Ai¹,

Ye²

2013

Asian Journal of Control

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Unfalsifying Pole Locations Using a Fading Memory Cost Function

Huang¹,

Chang²,

Safonov³

2013

Asian Journal of Control

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Given a feedback control system with an unknown plant, the problem of choosing a stabilizing controller is considered. Working within the framework of unfalsified adaptive control, we consider a finite-dimensional linear time invariant system as a special case of the standard adaptive configuration. A fading memory cost function is presented in which the influence of older data is reduced exponentially. With this cost function, the location of the poles can be detected with only input-output data. Compared with existing results, the cost function can detect changes affecting stability sooner and be used in adaptive switching control to improve the performance of controller switching.

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