Analysis and design of a linear input/output data-based predictive control

In this work, a subspace identification algorithm is reformulated from a co ntrol point of view. The proposed algorithm is referred to as an input/outp ut data-based predictive control, in which an explicit model of the system to be controlled is not calculated at any point in the algorithm. First, th e state estimation obtained by the subspace identification algorithm is ana lyzed in comparison with the receding-horizon-based estimation. For the sub space algorithm, it is well-known that a Kalman filter state is calculated by simple linear algebra under specific conditions. In general, however, it is shown that the present state estimation scheme gives a biased state est imate and that it has a structure similar to the best linear unbiased estim ation (BLUE) filter obtained by solving the least-squares problem analytica lly. With such an interpretation of the state estimation, we augment the in tegrated white noise model to add integral action to a linear input/output data-based predictive controller and use each the BLUE filter and the Kalma n filter as a stochastic observer for the unmeasured disturbance. The propo sed linear input/ouput data-based predictive controller is applied to the p roperty control of a continuous styrene polymerization reactor to demonstra te its improved performance.