A Gaussian radial basis function (RBF) neural networks-based local linearization autoregressive with exogenous (ARX) model is utilized for describing the dynamics of a class of smooth nonlinear and nonstationary industrial processes. The dynamics of the underlying processes may be treated as the system operating-point-dependent time-varying locally-linear behavior. The RBF-ARX model is a pseudo-linear ARX model identified offline, and its functional coefficients are composed of the operating-point-dependent RBF neural networks. The RBF-ARX model-based predictive control (MPC) design to the nonlinear process is presented, and stability analysis of the nonlinear MPC under some conditions is discussed. Especially, the feasibility and effectiveness as well as the significant performance improvements of the nonlinear MPC design proposed is demonstrated with a real industrial application to the nitrogen oxide (NO x ) decomposition (de-NO x ) process in thermal power plants.
Excellent control, minimal error from setpoint, of a boiler unit has always been an elusive goal. Adap tive Control System was applied to steam temperature control of utility boilers. In the control system, the adaptive control strategy is embedded on the DCS controllers in parallel with each conventional
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