A state space model based multistep adaptive predictive controller (MAPC) with disturbance modeling and Kalman filter prediction

A multistep adaptive predictive control strategy based on a state space mod el of the process has been developed. It can he compared with the Generaliz ed Predictive Control algorithm. The emphasis in the development of the pro posed control scheme is on modeling and elimination of disturbances. In the proposed scheme any prior information regarding the disturbances can be in corporated (by specifying certain polynomials and/or the noise covariances) . If no prior information is available then the unknown unmodeled effects ( such as noise, unmeasured load-disturbances and model process mismatch) can be represented by a residual model which can best be identified in a two-s tage setting. This approach leads to satisfactory modeling of disturbances and good regulation via predictive control. Some important features of the proposed algorithm are: (i) it uses a state space model which allows separa te modeling of u-to-y process dynamics, process and measurement noise; this is not possible in an ARMAX-type input/output model where process and meas urement noise appear lumped in the noise polynomial; (ii) it uses a Kalman Filter (KF) to generate the predictions of the output; the KF can be easily tuned via noise covariances and is a simpler and better alternative to spe cifying or estimating a noise polynomial; (iii) there is no need to solve a Diophantine identity on-line; the result is reduced computation; and (iv) if residual modeling is used it leads to simpler and improved way of handli ng disturbances. The proposed control algorithm is presented for the single -input, single-output case. Applying the algorithm to multivariable process es is straightforward. Simulation examples are included to illustrate the a dvantages and performance of the proposed control scheme.