In this paper, a ‘‘third generation’’ benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control ~MPC! scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction...

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is “linear” in the (max,+) algebra. In our previous work we have only considered MPC for the perturbationsfree case and for the ca...

Model predictive control (MPC) is presented as a robust, flexible decision framework for dynamically managing inventories and satisfying customer demand in demand networks. In this paper, a formulation and the benefits of an MPC-based, control-oriented tactical inventory management system meaningful to the semiconductor industry are presented via two significant examples. The translation of ava...

This paper describes a very detailed and comprehensive description of a model predictive controller (MPC) applied to the buck converter working in Continuous Conduction Mode (CCM) to optimize the performance of the converter. The converter is designed for a fixed switching frequency of 1 MHz. The proposed model predictive control technique achieves improved set point tracking with minimal overs...

In model-predictive control (MPC), achieving the best closed-loop performance under a given computational capacity is the underlying design consideration. This paper analyzes the MPC tuning problem with control performance and required computational capacity as competing design objectives. The proposed multi-objective design of MPC (MOD-MPC) approach extends current methods that treat control p...

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...