TY - JOUR

T1 - Forward path model predictive control using a non-minimal state space form

AU - Exadaktylos, V.

AU - Taylor, C. James

AU - Wang, Liuping

AU - Young, Peter C.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Model predictive control

KW - non-minimal state space

KW - constraints

KW - robustness

U2 - 10.1243/09596518JSCE674

DO - 10.1243/09596518JSCE674

M3 - Journal article

VL - 223

SP - 353

EP - 369

JO - Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

JF - Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering

SN - 0959-6518

IS - 3

ER -