Forward path model predictive control using a non-minimal state space form

This paper considers Model Predictive Control (MPC) using a Non-Minimal State Space (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative forward path structure. There is a close analogy with Proportional-Integral-Plus (PIP) control system design, which is also based on the definition of a NMSS model with two control structures. However, the MPC/NMSS approach has the advantage of handling system constraints at the design stage. Also, since the NMSS model is obtained directly from the identified transfer function model, the covariance matrix for the parameter estimates can be used to evaluate the robustness of the predictive control system to model uncertainty using Monte-Carlo simulation. The effectiveness of the approach is demonstrated by means of simulation examples, including the IFAC'93 benchmark and the ALSTOM nonlinear gasifier problem. For the simulation examples considered here, the forward path form preserves the good performance properties of the original MPC/NMSS controller, whilst at the same time yielding improved robustness