ROBUST MPC FOR STABLE LINEAR SYSTEMS

AUTOR(ES)

Rodrigues, M.A., Odloak, D.

FONTE

Brazilian Journal of Chemical Engineering

DATA DE PUBLICAÇÃO

2002-03

RESUMO

In this paper, a new model predictive controller (MPC), which is robust for a class of model uncertainties, is developed. Systems with stable dynamics and time-invariant model uncertainty are treated. The development herein proposed is focused on real industrial systems where the controller is part of an on-line optimization scheme and works in the output-tracking mode. In addition, the system has a time-varying number of degrees of freedom since some of the manipulated inputs may become constrained. Moreover, the number of controlled outputs may also vary during system operation. Consequently, the actual system may show operating conditions with a number of controlled outputs larger than the number of available manipulated inputs. The proposed controller uses a state-space model, which is aimed at the representation of the output-predicted trajectory. Based on this model, a cost function is proposed whereby the output error is integrated along an infinite prediction horizon. It is considered the case of multiple operating points, where the controller stabilizes a set of models corresponding to different operating conditions for the system. It is shown that closed-loop stability is guaranteed by the feasibility of a linear matrix optimization problem.

Documentos Relacionados

• New robust stable MPC using linear matrix inequalities
• Improved H2 and H¥ conditions for robust analysis and control synthesis of linear systems
• Robust MPC with output feedback.
• MPC adaptive - multimodels for control of nonlinear systems.
• ON THE STRUCTURED ROBUST STABILIZATION OF LINEAR SYSTEMS: A REVIEW AND FURTHER RESULTS