Due to the fast advancement of communication and information technology, intelligent
buildings have garnered great interest. These buildings can forecast weather, ambient …

Model predictive control is a form of control in which the current control action is obtained by
solving, at each sampling instant, a finite horizon open-loop optimal control problem, using …

More than 15 years after model predictive control (MPC) appeared in industry as an effective
means to deal with multivariable constrained control problems, a theoretical basis for this …

This paper studies finite-time stability analysis and finite-time stabilization of linear systems
by bounded linear time-varying feedback. On the one hand,(both local and global) finite-time …

Power consumption imposes a significant cost for data centers implementing cloud services,
yet much of that power is used to maintain excess service capacity during periods of low …

This paper gives an overview of robustness in Model Predictive Control (MPC). After
reviewing the basic concepts of MPC, we survey the uncertainty descriptions considered in …

We present a method for the construction of a robust dual-mode, receding horizon controller
which can be employed for a wide class of nonlinear systems with state and control …

Nature discovered feedback long ago. It created feedback mechanisms and exploited them
at all levels, that are central to homeostasis and life. As a technology, control dates back at …

Learning methods based on dynamic programming (DP) are receiving increasing attention
in artificial intelligence. Researchers have argued that DP provides the appropriate basis for …

The receding-horizon control strategy provides a relatively simple method for determining
feedback control for linear or nonlinear systems: the method is especially useful for the …