A Prediction-Correction Algorithm for Real-Time Model Predictive Control

TL;DR

This paper introduces a prediction-correction algorithm for real-time model predictive control that ensures fast convergence and stability, enabling control of faster systems with reduced computational resources.

Contribution

It adapts a prediction-correction method to continuous-time convex optimization in MPC, achieving quadratic convergence with low computational complexity and stability guarantees.

Findings

Quadratic convergence reduces iteration count for desired accuracy.

Algorithm maintains stability of the closed-loop system.

Numerical examples validate theoretical results.

Abstract

In this work we adapt a prediction-correction algorithm for continuous time-varying convex optimization problems to solve dynamic programs arising from Model Predictive Control. In particular, the prediction step tracks the evolution of the optimal solution of the problem which depends on the current state of the system. The cost of said step is that of inverting one Hessian and it guarantees, under some conditions, that the iterate remains in the quadratic convergence region of the optimization problem at the next time step. These conditions imply (i) that the variation of the state in a control interval cannot be too large and that (ii) the solution computed in the previous time step needs to be sufficiently accurate. The latter can be guaranteed by running classic Newton iterations, which we term correction steps. Since this method exhibits quadratic convergence the number of iterations to achieve a desired accuracy $\eta$ is of order $\log_2\log_2 1/\eta$, where the cost of each iteration is approximately that of inverting a Hessian. This grants the prediction-correction control law low computational complexity. In addition, the solution achieved by the algorithm is such that the closed loop system remains stable, which allows extending the applicability of Model Predictive Control to systems with faster dynamics and less computational power. Numerical examples where we consider nonlinear systems support the theoretical conclusions.