This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

Structural model uncertainty is prevalent in control design and arises from incomplete
knowledge of the system or the existence of different modes of dynamic behavior, such as …

This paper is about a real-time model predictive control (MPC) algorithm for a particular
class of model based controllers, whose objective consists of a nominal tracking objective …

Optimal control relies on a model, which is generally uncertain because of incomplete
knowledge of the system and changes in the dynamics over time. Probing the system under …

Model predictive control (MPC) is one of the most effective methods of dealing with
constrained control problems. Nevertheless, the uncertainty of the control system poses …

控制系统中存在的不确定性为其性能优化带来诸多问题. 自适应控制和鲁棒控制是针对系统存在
的不确定性而采取的不同设计策略; 前者没有充分考虑系统的未建模动态, 而后者往往是针对不 …

This article addresses the problem of designing a sensor fault‐tolerant controller for an
observation process where a primary, controlled system observes, through a set of …

In this paper, a new approach to the observability of dynamical systems with uncertain
sensors is developed. This approach is based on the redefinition of indistinguishability to …

A significant fraction of industrial MPC schemes employs linear prediction models. Closed
loop performance of a linear model based MPC scheme can deteriorate over a period of …

This research aims to enhance current methods for the optimal feedback control of complex
nonlinear dynamical systems via online parameter identifications. Accurate knowledge of …