An Improved Adaptive PID Controller Based on Online LSSVR with Multi RBF Kernel Tuning

Uçak, Kemal; Öke, Gülay

doi:10.1007/978-3-642-23857-4_8

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The dynamics of the NARX model should be decomposed into a N ARM A − L2 model in order to apply an inverse optimal controller [89]- [92]. Therefore, obtaining the The online LSSVR method has been used both to obtain the NARX model and then to convert it to the N ARM A − L2 model.…”

Section: Lssvr Based Narma-l2 Model Of Nonlinear Non-affine Systemsmentioning

confidence: 99%

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

Sanci,

Uçak,

Günel

2023

IEEE Access

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In this study, a novel inverse optimal controller based on NARMA-L2 modelling technique and online least squares support vector regression (LSSVR) method has been proposed for nonlinear nonaffine systems. Firstly, the nonlinear autoregressive with exogenous inputs (NARX) model of the system is obtained using online LSSVR method, then this model is decomposed into NARMA-L2 submodels. Hence, the non-affine system model is converted to a nonlinear affine system model. The obtained NARMA-L2 submodels are used in computing the inverse optimal control law. Furthermore, the parameters of the inverse optimal controller have also been optimized online using the Levenberg-Marquadt algorithm. The performance of the proposed LSSVR based inverse optimal controller using NARMA-L2 model has been evaluated by simulations carried out on two benchmark systems, and the results show that the LSSVR based NARMA-L2 model and inverse optimal controller attain good modelling and control performances.

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Section: Lssvr Based Narma-l2 Model Of Nonlinear Non-affine Systemsmentioning

confidence: 99%

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

Sanci,

Uçak,

Günel

2023

IEEE Access

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“…In this paper, multikernel RBF LSSVR algorithm includes initialization and adaptive update the design and procedure as follows [23].…”

Section: =̃−mentioning

confidence: 99%

“…Hence, a normalization of the data is required before presenting the input patterns to any statistical machine learning algorithm. In this experiment, 0-1 normalization method, denoted by (23), is utilized to preprocess:…”

Section: Data Selection and Standardizedmentioning

confidence: 99%

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A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance

Zhang

Chen

Karimi

2013

Abstract and Applied Analysis

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Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR andε-SVR, which is suitable for promotion online.

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“…This work, researched on the literature [24][25][26], proposed an amplifier evaluation strategy based on -SVR, presenting the superiority of -SVR about reducing support vector number. Moreover the modified RBF kernel function is also adopted which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate.…”

Section: Introductionmentioning

confidence: 99%

Research on Amplifier Performance Evaluation Based onδ-Support Vector Regression

Huo

Zhang

Karimi

2014

Abstract and Applied Analysis

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Focusing on the amplifier performance evaluation demand, a novel evaluation strategy based onδ-support vector regression (δ-SVR) is proposed in this paper. Lower computer calculation demand is considered firstly. And this is dealt with by the superiority ofδ-SVR which can be significantly improved on the number of support vectors. Moreover, the function ofδ-SVR employs the modified RBF kernel function which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the need of the number ofδ-SVR support vectors is the lowest among the other two methods LSSVR andε-SVR under obtaining nearly the same evaluation result. And this is also suitable for promotion computational speed.

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An Improved Adaptive PID Controller Based on Online LSSVR with Multi RBF Kernel Tuning

Cited by 9 publications

References 12 publications

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance

Research on Amplifier Performance Evaluation Based onδ-Support Vector Regression

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