Identification and application of bounded-parameter models
Automatica (Journal of IFAC)
Optimal estimation theory for dynamic systems with set membership uncertainty: an overview
Automatica (Journal of IFAC)
Brief paper: A parametric programming approach to moving-horizon state estimation
Automatica (Journal of IFAC)
Lagrangian duality between constrained estimation and control
Automatica (Journal of IFAC)
Hi-index | 22.15 |
In this article, we examine the effect of constraints on estimation and control methods based on quadratic penalty functions. We begin with estimation theory and analyze how constraints alter the statistical properties of the least squares estimates. It is shown that constraints can be used to formulate maximum likelihood (MLE) and maximum a posteriori (MAP) estimators for a variety of unimodal distributions. This provides greater flexibility over the assumption of normality inherent in the MLE and MAP interpretation of traditional least squares. We discuss how these ideas apply to state space models of dynamic systems. Possible applications for controllers that handle constraints are also discussed. A parameter estimation example is given to demonstrate the potential for improved performance over unconstrained least squares.