Self-Evolving Offset-Free Model Predictive Control with Model–Plant Mismatch for Dynamic Working-Point Change Tasks in Industrial Processes

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This study proposes a self-evolving offset-free model predictive control (MPC) algorithm for dynamic working-point change (DWPC) tasks in industrial processes. The algorithm mitigates disturbance impacts caused by model–plant mismatch (MPM) and enhances the dynamic performance of MPC by locating sequences (scenarios) similar to the current operational scenario from historical DWPC tasks and using them for multistep-ahead disturbance prediction. First, a disturbance-augmented state–space model guarantees the basic offset-free control behavior of MPC with MPM. Next, to enhance the MPC performance, a direct multistep-ahead disturbance prediction approach is proposed by combining historically similar DWPC task scenarios. Specifically, a dynamic autoencoder is constructed to extract private features from process scenarios and locate similar scenarios from historical DWPC tasks. Based on the located scenarios, the multistep-ahead disturbance and its uncertainty are directly predicted through multioutput Gaussian process regression. Finally, the obtained disturbance results are incorporated into the MPC framework, which continuously enhances MPC performance in DWPC tasks. Two case studies demonstrate the effectiveness of the proposed MPC.