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In this paper, the effects of tuning the kernel bandwidth for an online LSSVM are investigated. LSSVM is used to obtain a model of the system, and based on this model information, an adaptive PID is designed to control the plant. The kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by(More)
In this paper, inverse differential kinematic modeling is performed for a 7-DOF (Degrees of Freedom) redundant robot arm. Two intelligent identification methods, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used for modeling. The main strengths of SVR over ANN are that it doesn't get stuck at local minima and it has(More)
In this paper, ε — Support Vector Regression (SVR) method is employed to model a quadruple tank system. For a successful and reliable analysis and synthesis in control engineering, primarily, the correct estimation of system model is of great significance. SVR can be used as an important tool in modeling, since it has a good generalization(More)
In this study, a novel nonlinear autoregressive moving average (NARMA)-L2 controller based on online support vector regression (SVR) is proposed. The main idea is to obtain a SVR based NARMA-L2 model of a nonlinear single input single output system (SISO) by decomposing a single SVR which estimates the nonlinear autoregressive with exogenous inputs (NARX)(More)
This paper introduces a novel generalized self-tuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error(More)
In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a fuzzy PID controller has been proposed. Online LSSVR model is utilized to approximate the system Jacobian needed to tune controller parameters. The scaling coefficients of the controller have been tuned depending on K-step ahead future behavior(More)
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