Offset-free neural network-based nonlinear model predictive controller design using parameter adaptation

The performance of conventional nonlinear model predictive control $$\left( {{\text{NMPC}}} \right)$$ system relies heavily on the accuracy of the prediction model. In cases of significant plant-model mismatch, non-desirable responses may be observed in the controlled outputs. This paper proposes a parameter adaptation technique for tackling this problem. In the proposed approach, the output disturbance is selected as the adaptation parameter while the adaptation law is modelled as a function of the tracking error using a first-order difference equation. The adaptation law is integrated into a $$\mathrm{NMPC}$$ algorithm to achieve offset-free tracking. The effectiveness of the proposed scheme is demonstrated on two simulation case studies—a pH system and a continuously stirred tank reactor (CSTR); and an experimental cascaded two tank process. The simulation results obtained showed that the proposed scheme achieves zero offset in the face of significant plant-model mismatch arising from uncertainties in model parameters, unmeasured disturbances, and measurement noise and compared favourably with existing methods. The experimental results obtained during real-time implementation of the proposed control scheme corroborate this assertion and show its industrial applicability.