This paper presents a software library developed to implement model predictive control
(MPC) strategies in real-time. By using MPC, an appropriate optimal control law is implicitly …

Model Predictive Control (MPC) while being a very effective control technique can become
computationally demanding when a large prediction horizon is selected. To make the …

This letter describes an approach for parametrizing input and state trajectories in model
predictive control. The parametrization is designed to be invariant to time shifts, which …

The paper discusses dynamic real-time scheduling in the context of model predictive control
(MPC). Dynamic scheduling in this setting is motivated by the highly varying execution times …

This paper proposes an optimal control strategy for a differential-drive mobile robot. It is well
known that such a system can not be feedback stabilized by a smooth time-invariant control …

Many model predictive control (MPC) algorithms have been proposed in the literature
depending on the conditionality of the system matrix and the choice of its cost-function. This …

In order to effectively control nonlinear and multivariable models, and to incorporate
constraints on system states, inputs and outputs (bounds, rate of change), a suitable …

This tutorial consists of a brief introduction to the modern control approach called model
predictive control (MPC) and its numerical implementation using MATLAB. We discuss the …

Model predictive control (MPC) has been used in many industrial applications because of its
ability to produce optimal performance while accommodating constraints. However, its …

The computational demands of Model predictive control (MPC) are well known, and due to
its internal constrained optimiser, historically has been ill-suited for embedded controllers …