Sciweavers

AUTOMATICA
2005

Incorporating state estimation into model predictive control and its application to network traffic control

13 years 3 months ago
Incorporating state estimation into model predictive control and its application to network traffic control
Model predictive control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. This paper focuses on exploring the inclusion of state estimates and their interaction with constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. Using a gaussian assumption, the original problem is approximated by a standard deterministically-constrained MPC problem for the conditional mean process of the state. The state estimates' conditional covariances appear in tightening the constraints. `Closed-loop covariance' is introduced to reduce the infeasibility and the conservativeness caused b...
Jun Yan, Robert R. Bitmead
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2005
Where AUTOMATICA
Authors Jun Yan, Robert R. Bitmead
Comments (0)