An Adaptive Internal Model Control Based on LS-SVM

Sun, Changyin; Song, Jinya

doi:10.1007/978-3-540-72395-0_61

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…They optimized system model and controller parameters by backpropagation algorithm. In [27,28,29,30] nonlinear internal model control (NIMC) approach has been combined with the powerful generalization and modeling capacity of SVR. In [31,32,33,34,35] and [36] SVR methodology is utilized in model predictive control, in these studies mainly the models of the controlled systems have been obtained by SVR.…”

Section: Introductionmentioning

confidence: 99%

Online Support Vector Regression Based Adaptive NARMA-L2 Controller for Nonlinear Systems

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Günel

2021

Neural Process Lett

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NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the precision with which controlled systems' dynamics can be estimated. In this paper, online SVR is utilized to obtain controlled plant's subdynamics and consequently this information is used in the construction of NARMA-L2 controller. Hence functionality of NARMA-L2 controllers and high generalization capability of SVR are combined. Also, SVR formulates a convex optimization problem and therefore guarantees global optimum solution. The proposed method is assessed by performing simulations on a nonlinear CSTR system, the robustness of the designed controller is also tested under noisy and uncertainty conditions.

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