Journal of Optimization Theory and Applications
State-space interpretation of model predictive control
Automatica (Journal of IFAC)
Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Quasi-Min-Max MPC algorithms for LPV systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Robustness analysis on constrained model predictive control for nonholonomic vehicle regulation
ACC'09 Proceedings of the 2009 conference on American Control Conference
Multivariable model predictive control for integrating processes with input constraints
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Journal of Control Science and Engineering
Hi-index | 22.15 |
In this paper, we developed a model predictive controller, which is robust to model uncertainty. Systems with stable dynamics are treated. The paper is mainly focused on the output-tracking problem of a system with unknown steady state. The controller is based on a state-space model in which the output is represented as a continuous function of time. Taking advantage of this particular model form, the cost functions is defined in terms of the integral of the output error along an infinite prediction horizon. The model states are assumed perfectly known at each sampling instant (state feedback). The controller is robust for two classes of model uncertainty: the multi-model plant and polytopic input matrix. Simulations examples demonstrate that the approach can be useful for practical application.