A Proximal-Point Lagrangian Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control

TL;DR

This paper introduces a parallelizable nonconvex solver for bilinear model predictive control that enables real-time implementation by converting the problem into smaller, pre-computable quadratic programs, validated on HVAC and motor control systems.

Contribution

It proposes a novel parallel proximal-point Lagrangian solver with horizon-splitting for bilinear MPC, allowing efficient online solutions through offline pre-computation.

Findings

Solver achieves MPC updates at 10 ms.

Average solution time is 1.764 ms.

Validated on HVAC and motor control systems.

Abstract

Nonlinear model predictive control has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, to name a few. Running a bilinear MPC controller in real time requires solving a non-convex optimization problem within a limited sampling time. This paper proposes a novel parallel proximal-point Lagrangian based bilinear MPC solver via an interlacing horizon-splitting scheme. The resulting algorithm converts the non-convex MPC control problem into a set of parallelizable small-scale multi-parametric quadratic programs (mpQPs) and an equality-constrained linear-quadratic regulator problem. As a result, the solutions of mpQPs can be pre-computed offline to enable efficient online computation. The proposed algorithm is validated on a simulation of an HVAC system control. It is deployed on a TI LaunchPad XL F28379D microcontroller to execute speed control on a field-controlled DC motor, where the MPC updates at 10 ms and solves the problem in 1.764 ms on average and at most 2.088 ms.