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Design and application of offset-free model predictive control disturbance observation method. (English) Zbl 1346.93383

Summary: Model Predictive Control (MPC) with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents necessary conditions for offset-free model predictive control based on the model. By applying the disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the Dynamic Matrix Control (DMC) algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.

MSC:

93E11 Filtering in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93B40 Computational methods in systems theory (MSC2010)
93E25 Computational methods in stochastic control (MSC2010)
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