The default mode network (DMN) has been largely studied by imaging, but not yet by neurodynamics, using electroencephalography (EEG) functional connectivity (FC). mindfulness meditation (MM), a receptive, non-elaborative training is theorized to lower DMN activity. We explored: (i) the usefulness of EEG-FC for investigating the DMN and (ii) the MM-induced EEG-FC effects. To this end, three MM g...

This workshop introduces its audience to the theory, design and applications of model predictive control (MPC) under uncertainty. The workshop provides conceptual and technical principles governing rigorous and computationally effective methods for design of MPC under set–membership and probabilistic uncertainty. The theoretical fundamentals are carefully introduced and studied within the frame...

Model Predictive Control (MPC) is the de facto standard in advanced industrial automation systems. There are two main formulations of the MPC algorithm: an implicit one and an explicit MPC one. The first requires an optimization problem to be solved on-line, which is the main limitation when dealing with hard real-time applications. As the implicit MPC algorithm cannot be guaranteed in terms of...

In this paper we use bilevel programming to find the maximum difference between a model predictive controller (MPC) using a full model and an MPC using a reduced model. The results apply to MPC with quadratic cost function and linear model with linear constraints.

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 the MPC literature, and the techniques proposed for robust constraint handling, stability, and performance. The key concept of “closedloop prediction” is discussed at length. The paper concludes with some comments on future...

The Cu-based MOF loaded on macroporous carbon (MPC) creates novel Cu-MOF-MPC hybrids for the first time. The obtained Cu-MOF-MPC composites are used as electrocatalysts for the oxidation of NADH and reduction of H2O2 in neutral solution.

We develop a model predictive control (MPC) design for systems with discrete-time dynamics evolving on smooth manifolds. We show that the properties of conventional MPC for dynamics evolving on R are preserved and we develop a design procedure for achieving similar properties. We also demonstrate that for discrete-time dynamics on manifolds with Euler characteristic not equal to 1, there do not...

We present CODEV, a Matlab-based tool for verifying systems employing Model Predictive Control (MPC). The MPC solution is computed offline and modeled together with the physical system as a hybrid automaton, whose continuous dynamics may be nonlinear with a control solution that remains affine. While MPC is a widely used synthesis technique for constrained and optimal control in industry, our t...

This paper considers discrete-time nonlinear, possibly discontinuous, systems in closed-loop with Model Predictive Controllers (MPC). The aim of the paper is to provide a priori sufficient conditions for asymptotic stability in the Lyapunov sense and robust stability, while allowing for both the system dynamics and the value function of the MPC cost to be discontinuous functions of the state. T...

Due to climate change energy efficiency is becoming increasingly important. This is particularly true for the building system control sector, where a large energy saving potential exists. Model Predictive Control (MPC) is a new control technique in this sector that minimises the building energy use while taking into account comfort and other constraints through the use of a simplified building ...