ParNMPC : A Parallel Computing Toolbox for Nonlinear MPC

Version 1903-1 (Github: ParNMPC) has been released!

Introduction

ParNMPC is a MATLAB real-time optimization toolkit for nonlinear model predictive control (NMPC). The purpose of ParNMPC is to provide an easy-to-use environment for NMPC problem formulation, closed-loop simulation, and deployment.

With ParNMPC , you can define your own NMPC problem in a very easy way and ParNMPC will automatically generate self-contained C/C++ code for single- or multi-core CPUs.

ParNMPC is very fast even with only one core (the computation time is usually in the range of $\mu$ s), and a high speedup can be achieved when parallel computing is enabled.

Features

Symbolic problem representation
Automatic parallel C/C++ code generation with OpenMP
Fast rate of convergence (up to be superlinear)
Highly parallelizable (capable of using at most N cores, N is the # of discretization steps)
High speedup ratio
MATLAB & Simulink

Problems Supported in ParNMPC

The optimal control problem (OCP) supported in ParNMPC has the following form:

$\begin{align} \min_{x(\cdot),\ u(\cdot)}&\int^{T}_{0}L\left(u(t),x(t),p(t)\right)dt \\ \text{s.t.}\quad &x(0) = \bar{x}_0,\\ &M\left(u(t),x(t),p(t)\right)\dot{x}(t)=f\left(u(t),x(t),p(t)\right),\quad t\in[0,T],\\ & C(u(t),x(t),p(t))=0,\quad t\in[0,T],\\ & G\left(u(t),x(t),p(t)\right)\geq 0,\quad t\in[0,T]. \end{align}$

Here,

Sign	Size	Description	Sign	Size	Description
$u$	`[dim.u,1]`	Input vector	$L$	`[1,1]`	Cost function
$x$	`[dim.x,1]`	State vector	$C$	`[dim.mu,1]`	Equality constraint function
$p$	`[dim.p,1]`	Given parameter vector	$f$	`[dim.x,1]`	Dynamics
$T$	`[1,1]`	Prediction horizon	$M$	`[dim.x,dim.x]`	(Optional, e.g., Lagrange model)
$\bar{x}_0$	`[dim.x,1]`	Current state vector	$G$	`[dim.z,1]`	Polytopic constraint function ( $G$ is linear in $u$ and $x$ )

Note

With the parameter $p$ , you can achive

Time-variant dynamics and references
Terminal cost function and constraint

How ParNMPC works

ParNMPC discretizes the OCP defined above into the following problem with $N$ steps:

$\begin{align} \min_{x,\ u}\sum_{i=1}^{N}&L(u_i,x_i,p_i) \\ \text{s.t.}\quad & x_0 = \bar{x}_0,\\ &F(x_{i-1},u_i,x_i,p_i)=0, \quad i\in\{1,\cdots,N\},\\ & C(u_i,x_i,p_i)=0,\quad i\in\{1,\cdots,N\},\\ & G(u_i,x_i,p_i) \geq 0,\quad i\in\{1,\cdots,N\}. \end{align}$

Here, $F(x_{i-1},u_i,x_i,p_i)=0$ is the discretized state-space model. ParNMPC solves the discretized OCP using the parallel primal-dual interior-point method, where the inequality constraints $G(u_i,x_i,p_i) \geq 0$ are transferred into logarithmic barrier functions with a barrier parameter $\rho$ .

Variables

Variable	Size	Description
$\lambda$	`[dim.lambda,N]`	Lagrange multiplier corresponding to $F$
$\mu$	`[dim.mu,N]`	Lagrange multiplier corresponding to $C$
$u$	`[dim.u,N]`
$x$	`[dim.x,N]`
$z$	`[dim.z,N]`	Lagrange multiplier corresponding to $G$
$\Lambda$ (LAMBDA)	`[dim.x,dim.x,N]`	Sensitivity