PRESAS: Block-Structured Preconditioning of Iterative Solvers within a Primal Active-Set Method for fast MPC

TL;DR

This paper introduces PRESAS, a block-structured preconditioning method within a primal active-set framework, enabling fast and reliable solution of structured quadratic programs in model predictive control, suitable for real-time vehicle applications.

Contribution

It proposes a novel block-structured preconditioning technique with rank-one updates for efficient iterative solving of MPC QPs, including an augmented Lagrangian approach for initialization.

Findings

PRESAS outperforms existing QP solvers in computational speed.

The method is real-time capable on embedded vehicle control hardware.

Numerical experiments confirm high reliability and efficiency.

Abstract

Model predictive control (MPC) for linear dynamical systems requires solving an optimal control structured quadratic program (QP) at each sampling instant. This paper proposes a primal active-set strategy (PRESAS) for the efficient solution of such block-sparse QPs, based on a preconditioned iterative solver to compute the search direction in each iteration. Rank-one factorization updates of the preconditioner result in a per-iteration computational complexity of $\mathcal{O}(N m^2)$, where $m$ denotes the number of state and control variables and $N$ the number of control intervals. Three different block-structured preconditioning techniques are presented and their numerical properties are studied further. In addition, an augmented Lagrangian based implementation is proposed to avoid a costly initialization procedure to find a primal feasible starting point. Based on a standalone C code implementation, we illustrate the computational performance of PRESAS against current state of the art QP solvers for multiple linear and nonlinear MPC case studies. We also show that the solver is real-time feasible on a dSPACE MicroAutoBox-II rapid prototyping unit for vehicle control applications, and numerical reliability is illustrated based on experimental results from a testbench of small-scale autonomous vehicles.