Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and unmeasured stochastic disturbances. Using the dual mode controller concept, the formulation applies a terminal constraint at the end of finite horizon prediction so that outputs reach their steady values asymptotically. The control strategy has been applied in a simulation of a thermal power plant, which is a complex multivariable system based on a non-linear physical model. Although the theoretical results are not entirely new, a complete stable MPC formulation along with its application in a realistic large-scale system is not readily available in MPC literature. A set of simulation results demonstrates the effectiveness of the formulated strategy and compares its performance with finite horizon MPC.

title = "A Complete Formulation of Non-Linear Model-Based Stable Predictive Control Strategy",

abstract = "This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and unmeasured stochastic disturbances. Using the dual mode controller concept, the formulation applies a terminal constraint at the end of finite horizon prediction so that outputs reach their steady values asymptotically. The control strategy has been applied in a simulation of a thermal power plant, which is a complex multivariable system based on a non-linear physical model. Although the theoretical results are not entirely new, a complete stable MPC formulation along with its application in a realistic large-scale system is not readily available in MPC literature. A set of simulation results demonstrates the effectiveness of the formulated strategy and compares its performance with finite horizon MPC.",

author = "G Prasad and GW Irwin and E Swidenbank and BW Hogg",

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Complete Formulation of Non-Linear Model-Based Stable Predictive Control Strategy

AU - Prasad, G

AU - Irwin, GW

AU - Swidenbank, E

AU - Hogg, BW

PY - 2001/9

Y1 - 2001/9

N2 - This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and unmeasured stochastic disturbances. Using the dual mode controller concept, the formulation applies a terminal constraint at the end of finite horizon prediction so that outputs reach their steady values asymptotically. The control strategy has been applied in a simulation of a thermal power plant, which is a complex multivariable system based on a non-linear physical model. Although the theoretical results are not entirely new, a complete stable MPC formulation along with its application in a realistic large-scale system is not readily available in MPC literature. A set of simulation results demonstrates the effectiveness of the formulated strategy and compares its performance with finite horizon MPC.

AB - This paper reports a complete formulation of a model predictive control strategy having guaranteed nominal asymptotic stability. The formulation includes a successive linearisation procedure to obtain a linear model from a non-linear plant model. It gives a complete state-space derivation including long-range prediction, trajectory tracking and modelling of both measured feedforward disturbances and unmeasured stochastic disturbances. Using the dual mode controller concept, the formulation applies a terminal constraint at the end of finite horizon prediction so that outputs reach their steady values asymptotically. The control strategy has been applied in a simulation of a thermal power plant, which is a complex multivariable system based on a non-linear physical model. Although the theoretical results are not entirely new, a complete stable MPC formulation along with its application in a realistic large-scale system is not readily available in MPC literature. A set of simulation results demonstrates the effectiveness of the formulated strategy and compares its performance with finite horizon MPC.