Patent ID: 6643554
Filing Date: 2003-11-04
Classification: G05B

Abstract:
In a method of controlling a marginally stable industrial process comprising the steps of 1) setting an initial state vector; 2) setting an initial parameter vector; 3) setting an initial prediction parameter gain; 4) set the initial covariance matrix; 5) performing a state update 6) estimating the model error, 7) updating the parameter vector, 8) updating the co-variance matrix, and 9) updating the controller output, the improvement comprising the steps of:a) Reading the process input, process output, process measured disturbance and the set point at the previous sample tine; b) Scaling the process input, process output, process measured disturbance to internal variables corresponding for process input, process output and process measured disturbances, respectively; c) When the controller is just enabled or a reset flag is raised ten Reading the tuning parameters from the configuration file; d) Computing a state space form of the orthonormal Laguerre network A, B, C(k), A_FFX, B_FFX, C_FFX(k), A_STS, B_STS, C_STS(k) where C(k), C_FFX(k), C_STS(k) are calculated from one of the following: loaded from a previously saved model; loaded from an initial model; initialised to [0,0,0, . . . 0]e) Computing control related matrices SB, SA, SS, K, SB_FFX, SA_FFX, SS_FFX, K_FFX, SB_STS, SA_STS SS_STS, K_STS; f) Initializing C_M(k) with C(k), C_M_STS(k) with C_STS(k) and C_M_FFX(k) with C_FFX(k); g) Clearing the state vector L(k), L_FFX(k), L_STS(k) used during control; and h) Updating the models states as follows: L&af;(k)=A*L&af;(k-1)+B*CV&af;(k-1)L_FFX&it;(k)=A_FFX*L_FFX&it;(k-1)+B_FFX*FFX&af;(k-1)L_STS&it;(k)=A_STS*L_STS&it;(k-1)+B_STS*DV&af;(k-1)where A, A_FFX, A_STS represent the state and B, B_FFX, B_STS the input matrices of the corresponding state space representation of the Laguerre networks generated for the same pole but different number of filters, if required; i) Calculating the models output estimation: Y_EST&it;(k)=Y_EST&it;(k-1)+C&af;(k)*L&af;(k)Y_EST&it;â€ƒ&it;_FFX&it;â€ƒ&it;(k)=â€ƒ&it;Y_EST&it;â€ƒ&it;_FFX&it;â€ƒ&it;(k-â€ƒ&it;1)+â€ƒ&it;C_FFX&it;â€ƒ&it;(k)*â€ƒ&it;L_FFX&it;â€ƒ&it;(k)&it;&NewLine;&it;Y_EST&it;_STS&it;(k)=Y_EST&it;_STS&it;(k-1)+C_STS&it;(k)*L_STS&it;(k)j) Updating the input of the unknown disturbance model: DV(k)=(Yâ€”EST(k)+Yâ€”ESTâ€”FFX(k)+Yâ€”ESTâ€”STS(k))âˆ’PV(k); k) Repeating steps h. to j. a plurality of times for the convergence of the state estimators; l) Iterate the computation of the input to the unknown disturbance model also for convergence purposes; m) Computation of the prediction parameter gain: BETA(k) C(k)*SB(k).