Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Generalized predictive control—Part II. Extensions and interpretations
Automatica (Journal of IFAC)
Predictive control: a unified approach
Predictive control: a unified approach
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Fuzzy model predictive control
IEEE Transactions on Fuzzy Systems
An improved adaptive PID controller based on online LSSVR with multi RBF Kernel tuning
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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In this study, the previously proposed Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) method [1] has been applied in controlling the experimental three-tank system. The SVM regression algorithms have been successfully employed in modeling nonlinear systems due to their advantageous peculiarities such as assurance of the global minima and higher generalization capability. Thus, the fact that better modeling accuracy yields better control performance has motivated us to use an SVM model in the GPC loop [1]. In the method, the SVM model of the unknown plant is used to predict future behavior of the plant and also to extract the gradient information which is used in the Cost Function Minimization (CFM) block. The experimental results have revealed that SVM-Based GPC provides very high performance in controlling the system, i.e., the liquid level of the system can track the different types of reference inputs with very small transientand steady-state errors even in a noisy environment when it is controlled by SVM-Based GPC.