Nonlinear Model Predictive Control (NMPC) is a powerful control framework, which strongly relies on a good model of the system dynamics. In the case, such a model is not available apriori, non-parametric regression using Bayesian regression or Gaussian Processes (GPs) have been shown promising in inferring the dynamics from collected data. An advantage of Bayesian methods and GPs over other reg...

This paper presents a Nonlinear Model Predictive Control (NMPC) for redundant robotic manipulators. Using NMPC, the end-effector of the robotic manipulator tracks a predefined geometry path in Cartesian space in such a way that no collision with obstacles in the workspace and no singular configurations for the robot occurs. Nonlinear dynamic of the robot, including actuators dynamic, is also co...

Model predictive control is a very interesting research area for its practical applications in the context of process control of industrial plants. The book, object of the review, covers a specific branch of Model Predictive Control, that is Nonlinear Model Predictive Control (NMPC). This review is organized in two sections: the Overview where the contents of the book are examined and the Concl...

Model predictive control (MPC) is an excellent approach for controlling systems with constraints. For nonlinear systems, nonlinear model predictive control (NMPC) is a natural solution, but it seems to be not suitable for system with relative fast dynamics. The main disadvantage is the time needed for solving the corresponding optimisation problem. Different approaches are used to simplify the ...

Accurate trajectory-tracking control for quadrotors is essential safe navigation in cluttered environments. However, this challenging agile flights due to nonlinear dynamics, complex aerodynamic effects, and actuation constraints. In article, we empirically compare two state-of-the-art frameworks: the nonlinear-model-predictive controller (NMPC) differential-flatness-based (DFBC), by tracking a...

Representing the uncertainties with a set of scenarios, the optimization problem resulting from a robust nonlinear model predictive control (NMPC) strategy at each sampling instance can be viewed as a large-scale stochastic program. This paper solves these optimization problems using the parallel Schur complement method developed to solve stochastic programs on distributed and shared memory mac...

In this contribution a hierarchical approach to the control of the molecular weight in a semi-batch emulsion polymerization is derived, which ensures minimum batch time. Rather than performing a numerical optimization to determine an optimal trajectory for the process at any point in time, the existing boundary conditions are examined and used to determine the time optimal operation at each tim...

This work focuses on the nonlinear control of instabilities in the combustion of a diluted dimethyl ether (DME) / air mixture under low temperature conditions. A nonlinear model predictive control (NMPC) is utilized to stabilize the combustion in simulation with the aim to specify minimum actuation dynamic requirements and develop control strategies for an existing experimental chemical reactor.

In the last decade a lot of attention was given to non-linear model predictive control. On one hand, in many applications linear MPC does not suffice to achieve the control goals over a wide range of operating conditions, while on the other hand many academic challenges remained in the area of NMPC, such as stability, computational complexity, etc... This paper discusses the industrial implemen...