Offset-free multistep nonlinear model predictive control under plant-model mismatch

Abstract

A multistep nonlinear model predictive control (MPC) framework is developed to achieve steady-state offsetfree control in the presence of plant–model mismatch. Our formulation explicitly accounts for the effect of plant–model mismatch by involving the output feedback error, which is expressed as the difference between the measured process output and the predicted model output at the previous sampling instance, in the multistep model recursive prediction. The proposed scheme is capable of improving the performance of nonlinear MPC, because the plant–model mismatch is effectively compensated through the recursive prediction propagation. We prove that this formulation is able to remove the steady-state error to achieve offset-free control. The proposed nonlinear MPC framework is applied to a highly nonlinear two-input two-output continuous stirred tank reactor, in comparison with other MPC implementations. The results obtained demonstrate that the proposed technique outperforms some existing popular MPC schemes and can realise offset-free control even under significant plant–model mismatch and unmeasured disturbances

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