Offset-free ARX-based adaptive model predictive control applied to a nonlinear process

An adaptive steady state constrained autoregressive with extra input (SSARX) model is developed and embedded into a model predictive control (MPC) framework to regulate a nonlinear process for setpoint tracking. The resulting linear MPC can minimize the deviation of output from the setpoint along the entire prediction horizon and finally achieve zero-offset control even with model mismatch. Our contributions lie in three aspects: First, we develop a steady state constrained ARX model identification method. Second, we design an information matrix based index for model re-identification using online closed-loop data. Third, when steady state input values are unknown, our algorithm can adaptively adjust parameters in the objective function and model to track the setpoint crossing a wide range of operating conditions. A fermenter control problem is studied to demonstrate the effectiveness of the proposed approach.