Human Center of Mass Trajectory Models Using Nonlinear Model Predictive Control

Model predictive control of nonlinear systems using piecewise linear models

We consider the problem of controlling nonlinear systems which are modeled as a set of piecewise linear (PL) or affine systems using model predictive control (MPC). The paper reviews recent results on the analysis and control of PL systems, which can model a wide range of practically relevant nonlinear systems. Using techniques from the theory of linear matrix inequalities (LMIs), we develop a ...

Nonlinear Model Predictive Control with Probabilistic Models

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...

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Trajectory tracking of autonomous vessels using model predictive control ⋆

Autonomous surface vehicles are with increasing popularity being seen in various applications where automatic control plays an important role. In this paper the problem of twodimensional trajectory tracking for autonomous marine surface vehicles is addressed using Model Predictive Control (MPC). At each time step, the reference trajectories of a vessel are assumed to be known over a finite time...

Nonlinear Model Predictive Control