An Evolutionary Algorithm for Actuator-Sensor-Communication Co-Design in Distributed Control
This paper studies the co-design of actuators, sensors, and communication in the distributed setting, where a networked plant is partitioned into subsystems with sub-controllers interacting with other sub-controllers. The objective is to jointly minimize control cost (i.e., LQ cost) and material cost (i.e., number of actuators, sensors, and communication links used). We approach this using an evolutionary algorithm that selectively prunes a baseline dense LQR controller, and provide convergence and stability analyses. Our approach is validated in simulations; it substantially outperforms naive pruning (over 80% in most cases), and performs similarly as greedy pruning but with much-improved scalability.
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