Abstract dynamic programming models under commutativity conditions
SIAM Journal on Control and Optimization
Journal of Optimization Theory and Applications
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Optimal receding horizon filter for continuous-time nonlinear stochastic systems
SIP'07 Proceedings of the 6th Conference on 6th WSEAS International Conference on Signal Processing - Volume 6
Constrained Nonlinear State Estimation --- A Differential Evolution Based Moving Horizon Approach
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Implementation of FIR control for H∞output feedback stabilization of linear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Lagrangian duality between constrained estimation and control
Automatica (Journal of IFAC)
Simultaneous state and input estimation of hybrid systems with unknown inputs
Automatica (Journal of IFAC)
Temporal sampling issues in discrete nonlinear filtering
Automatica (Journal of IFAC)
Computers and Electronics in Agriculture
Event-triggered maximum likelihood state estimation
Automatica (Journal of IFAC)
Hi-index | 22.15 |
This article considers moving horizon strategies for constrained linear state estimation. Additional information for estimating state variables from output measurements is often available in the form of inequality constraints on states, noise, and other variables. Formulating a linear state estimation problem with inequality constraints, however, prevents recursive solutions such as Kalman filtering, and, consequently, the estimation problem grows with time as more measurements become available. To bound the problem size, we explore moving horizon strategies for constrained linear state estimation. In this work we discuss some practical and theoretical properties of moving horizon estimation. We derive sufficient conditions for the stability of moving horizon state estimation with linear models subject to constraints on the estimate. We also discuss smoothing strategies for moving horizon estimation. Our framework is solely deterministic.