An Evolutionary Algorithm for Actuator-Sensor-Communication Co-Design in Distributed Control

TL;DR

This paper presents an evolutionary algorithm for co-designing actuators, sensors, and communication links in distributed control systems, optimizing control and material costs with proven convergence and stability.

Contribution

It introduces a novel evolutionary pruning method for dense LQR controllers, including modifications for unstable plants, validated through simulations.

Findings

Co-design of a 98-state model completed in seconds on a standard laptop.

The co-design method outperforms naive pruning by over 50%.

The approach includes convergence and stability analyses for the algorithm.

Abstract

This paper studies the co-design of actuators, sensors, and communication in the distributed setting, where a networked plant is partitioned into subsystems each equipped with a sub-controller interacting with other sub-controllers. The objective is to jointly minimize control cost (measured by LQ cost) and material cost (measured by the number of actuators, sensors, and communication links used). We approach this using an evolutionary algorithm to selectively prune a baseline dense LQR controller. We provide convergence and stability analyses for this algorithm. For unstable plants, controller pruning is more likely to induce instability; we provide an algorithm modification to address this. The proposed methods is validated in simulations. One key result is that co-design of a 98-state swing equation model can be done on a standard laptop in seconds; the co-design outperforms naive controller pruning by over 50%.