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Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.
Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control (ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm (SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix. This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.