An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works.

A nonlinear model predictive controller computes optimal control moves across the prediction horizon using a nonlinear prediction model, a nonlinear cost function, and nonlinear constraints.

The purpose of this chapter is to give an introduction to two of the most powerful tools that can be used to address nonlinear control and estimation problems - nonlinear model predictive control (NMPC) …

May 2, 2025 · The efficient computation of parametric solution sensitivities is a key challenge in the integration of learning-enhanced methods with nonlinear model predictive control (MPC), as their …

Apr 11, 2011 · In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. We start by defining a basic NMPC algorithm for constant reference and continue by...

Oct 1, 2022 · In this paper, a practical nonlinear model predictive control with iterative nonlinear prediction and linearization is proposed, considering a long short-term memory (LSTM) artificial …

Nonlinear Model Predictive Control (NMPC) is an optimization-based feedback control method designed for nonlinear systems, focusing on stabilization and tracking. In tracking control, the objective is to …

Using nonlinear MPC, you can: Simulate closed-loop control of nonlinear plants under nonlinear costs and constraints. Plan optimal trajectories by solving an open-loop constrained nonlinear optimization …

May 25, 2024 · In this tutorial series, we explain how to formulate and numerically solve different versions of the nonlinear Model Predictive Control (MPC) problem. We implement the solution in …

In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way. We start by defining a basic NMPC algorithm for constant reference and continue by formalizing state and …