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This paper gives stability analysis of the nonlinear predictive control strategy based on the off-line identified RBF-ARX model which is a pseudo-linear time-varying ARX model with system working-point dependent Gaussian RBF neural network style coefficients. The predictive controller doesn't require on-line parameter estimation; it may be applied to a(More)
In this paper an approximate innovation method is introduced for the estimation of diffusion processes given a set of discrete and noisy observations of some of their components. The method is based on a recent extension of Local Linearization filters to the general case of continuous-discrete state space models with multiplicative noise. This filtering(More)
We address the issue of testing for nonlinearity in time series from continuous dynamics and propose a quantitative measure for nonlinearity which is based on discrete parametric modelling. The well-known problems of modelling continuous dynamical systems by discrete models are addressed by a subsampling approach. This measure should preferably be combined(More)
This paper considers modeling and control problems of the non-stationary nonlinear processes whose dynamics depends on the working point. A hybrid RBF-ARX model-based predictive control (MPC) strategy without resorting to on-line parameter estimation for this kind of processes is presented. The RBF-ARX model is composed of the RBF networks and a rather(More)
For nonlinear thermal power plants whose dynamics vary with load demand, a load-dependent exponential ARX (Exp-ARX) model which can exactly describes the nonlinear properties of the plants is presented. The Exp-ARX model requires only off-line identification. Based on the model, a constrained multivariable generalized predictive control (CMGPC) strategy is(More)
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