Automatic Software and Computing Hardware Co-design for Predictive Control

TL;DR

This paper introduces an automated co-design framework for Model Predictive Control that optimizes both software and hardware parameters to balance computational efficiency and control performance.

Contribution

It presents a novel multi-objective optimization approach, BiMADS, for automating MPC software and hardware co-design, improving over traditional manual tuning methods.

Findings

Optimization-based design outperforms Latin Hypercube Sampling.

The framework effectively balances resource usage and control quality.

Test studies include CPU and FPGA implementations.

Abstract

Model Predictive Control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems, while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often (i) limits the application scope to slow dynamical systems and/or (ii) results in expensive computational hardware implementations. Traditional MPC design is based on manual tuning of software and computational hardware design parameters, which leads to suboptimal implementations. This paper proposes a framework for automating the MPC software and computational hardware co-design, while achieving the optimal trade-off between computational resource usage and controller performance. The proposed approach is based on using a multi-objective optimization algorithm, namely BiMADS. Two test studies are considered: Central Processing Unit (CPU) and Field-Programmable Gate Array (FPGA) implementations of fast gradient-based MPC. Numerical experiments show that optimization-based design outperforms Latin Hypercube Sampling (LHS), a statistical sampling-based design exploration technique.