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Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks
Authors:Wang Yongji  WANG Hong
Abstract:A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.
Keywords:pseudolinear neural networks (PNN)  nonlinear model predictive control  continuous stirred tank reactor (CSTR)  asymptotic stability  neural networks  based  asymptotical stability  model predictive control  control strategy  nonlinear systems  simulation  reaction  continuous  stirred tank  reactor  closed loop  Lyapunov theory  sufficient condition  predictive control system  recursive  computation  Jacobian matrix  second order  optimization
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