A new inferential 2-step multiple input/multiple output (MIMO) model predictive control (MPC) of the particle size distribution (PSD) in emulsion polymerization processes is proposed. The bulk-like model describing the PSD is used with the material balances of initiator, radicals, monomer and surfactant. The inferential 2-step control strategy uses two measurements available online (without del...

Model predictive control (MPC) is a widely used control design method in the process industry. Its main advantage is that it allows the inclusion of constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. We extend MPC to max-min-plus discrete event systems. In general the resulting optimization problems are nonlinear and nonconvex. However, if the state equations a...

We combine conditional state density construction with an extension of the Scenario Approach for stochastic Model Predictive Control to nonlinear systems to yield a novel particle-based formulation of stochastic nonlinear output-feedback Model Predictive Control. Conditional densities given noisy measurement data are propagated via the Particle Filter as an approximate implementation of the Bay...

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted funct...

Fast speed drives for Permanent Magnet Synchronous Motor (PMSM) is a crucial performance for the electric traction systems. In this paper, PMSM is derived with a Model-based Predictive Control (MPC) technique. Fast speed tracking is achieved through optimization of the DC source utilization using MPC. The technique is based on predicting the optimum voltage vector applied to the driver. Control...

After several years of efforts, constrained model predictive control (MPC), the de facto standard algorithm for advanced control in process industries, has finally succumbed to rigorous analysis. Yet successful practical implementations of MPC were already in place almost two decades before a rigorous stability proof for constrained MPC was published. What is then the importance of recent theor...

In this paper we present a framework for achieving constrained optimal real-time control for large-scale systems with fast dynamics. The methodology uses the explicit solution of the model predictive control (MPC) problem combined with model reduction, in an output-feedback implementation. The explicit solution of the MPC problem leads to online MPC functionality without having to solve an opti...

This paper presents a model predictive controlled three-level three-phase active neutral-point-clamped (ANPC) inverter for distributing the voltage stress among the semiconductor power switches as well as balancing the neutralpoint voltage. The model predictive control (MPC) concept uses the discrete variables and effectively operates the ANPC inverter by avoiding any linear controller or modul...

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 ...

Inverted pendulum is a non-linear, multivariable and unstable device, a model predictive control (MPC) performance evaluation and tuning method for inverted pendulum device is proposed. MPC was designed to control the inverted pendulum device, and the minimum variance covariance constrained control (MVC) was applied to evaluate the performance of the MPC controller and tune its parameters. The ...