Model Predictive Path Integral Control: From Theory to Parallel Computation

Model Predictive Path Integral Control using Covariance Variable Importance Sampling

In this paper we present a Model Predictive Path Integral (MPPI) control algorithm that is derived from the path integral control framework and a generalized importance sampling scheme. In order to operate in real time we parallelize the sampling based component of the algorithm and achieve massive speed-up by using a Graphical Processor Unit (GPU). We compare MPPI against traditional model pre...

Nonlinear Model Predictive Control: From Theory to Application

─While linear model predictive control is popular since the 70s of the past century, only since the 90s there is a steadily increasing interest from control theoreticians as well as control practitioners in nonlinear model predictive control (NMPC). The practical interest is mainly driven by the fact that today’s processes need to be operated under tight performance specifications. At the same ...

Path Integral Control

Path Integral Density Functional Theory

A new method ( PI-DFT ) which combines path integrals and density functional theory is proposed as a pathway to many fields of physics. Within path integral theory it is possible to construct particle densities without explicitly calculating individual wave functions. These densities can directly be used as an input to energy density functionals. Thus our method makes full use of the theorem of...

Analysis of explicit model predictive control for path-following control

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can redu...