Support vector machines‐based generalized predictive control

İplikçi, Serdar

doi:10.1002/rnc.1094

Cited by 59 publications

(42 citation statements)

References 39 publications

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“…By following Reference [18], consider a non-linear system represented by the NARX model y n ¼ f ðu n ; . .…”

Section: Generalized Predictive Controlmentioning

confidence: 99%

“…In this subsection, the SVM approach to generalized predictive control will be elucidated by means of a comprehensible formulation as given in Reference [18]. If the current state vector is formed as c n ¼ ½u n u nÀ1 .…”

Section: The Svm-based Gpc Formulationmentioning

confidence: 99%

“…Recently, support vector machines have gained much attention due to their great potential of approximation, and have been used in the GPC scheme [18] for controlling non-linear systems. However, obtained models in these approaches are fixed and they cannot adapt to the changes in the plant dynamics and/or operating conditions, which may become a major disadvantage when the changes are not negligible.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this study is to introduce an adaptive GPC method by combining SVM-based GPC approach [18] with the online SVM training algorithm [25]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Online trained support vector machines-based generalized predictive control of non-linear systems

İplikçi

2006

Int. J. Adapt. Control Signal Process.

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In this work, an online support vector machines (SVM) training method (Neural Comput. 2003; 15: 2683-2703, referred to as the accurate online support vector regression (AOSVR) algorithm, is embedded in the previously proposed support vector machines-based generalized predictive control (SVM-Based GPC) architecture (Support vector machines based generalized predictive control, under review), thereby obtaining a powerful scheme for controlling non-linear systems adaptively. Starting with an initially empty SVM model of the unknown plant, the proposed online SVM-based GPC method performs the modelling and control tasks simultaneously. At each iteration, if the SVM model is not accurate enough to represent the plant dynamics at the current operating point, it is updated with the training data formed by persistently exciting random input signal applied to the plant, otherwise, if the model is accepted as accurate, a generalized predictive control signal based on the obtained SVM model is applied to the plant. After a short transient time, the model can satisfactorily reflect the behaviour of the plant in the whole phase space or operation region. The incremental algorithm of AOSVR enables the SVM model to learn the new training data pair, while the decremental algorithm allows the SVM model to forget the oldest training point. Thus, the SVM model can adapt the changes in the plant and also in the operating conditions. The simulation results on non-linear systems have revealed that the proposed method provides an excellent control quality. Furthermore, it maintains its performance when a measurement noise is added to the output of the underlying system.

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“…By following Reference [18], consider a non-linear system represented by the NARX model y n ¼ f ðu n ; . .…”

Section: Generalized Predictive Controlmentioning

confidence: 99%

Section: The Svm-based Gpc Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The aim of this study is to introduce an adaptive GPC method by combining SVM-based GPC approach [18] with the online SVM training algorithm [25]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online trained support vector machines-based generalized predictive control of non-linear systems

İplikçi

2006

Int. J. Adapt. Control Signal Process.

Self Cite

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“…Further, the margin maximum problem can be solved in any high-dimensional space by introducing a kernel function [95,96]. With a nonlinear kernel function, the low-dimensional input space is nonlinearly transformed into a high-dimensional feature space such that the probability that the feature space is linear separable becomes higher.…”

Section: Svmsmentioning

confidence: 99%

Advances in Artificial Neural Networks – Methodological Development and Application

Huang

2009

Algorithms

160

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Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological engineering.

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Kernel learning adaptive one‐step‐ahead predictive control for nonlinear processes

Wang

2008

Asia-Pacific J Chem Eng

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A Kernel learning adaptive one-step-ahead Predictive Control (KPC) algorithm is proposed for the general unknown nonlinear processes. The main structure of the KPC law is twofold. A one-step-ahead predictive model is first obtained by using the kernel learning (KL) identification framework. An analytical control law is then derived from Taylor linearization method, resulting in an efficient computation for on-line implementation. The convergence analysis of the KPC control strategy is presented, meanwhile a new concept of adaptive modification index is proposed to improve the tracking ability of KPC and reject the unknown disturbance. This simple KPC scheme has few parameters to be chosen and small computation scale, which make it very suitable for real-time control. Numerical simulations compared with a well-tuned proportional-integral-derivative (PID) controller on a nonlinear chemical process show the new KPC algorithm exhibits much better performance and more satisfactory robustness to both additive noise and unknown process disturbance. 

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Support vector machines‐based generalized predictive control

Cited by 59 publications

References 39 publications

Online trained support vector machines-based generalized predictive control of non-linear systems

Online trained support vector machines-based generalized predictive control of non-linear systems

Advances in Artificial Neural Networks – Methodological Development and Application

Kernel learning adaptive one‐step‐ahead predictive control for nonlinear processes

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