Computation-Accuracy Trade-Off in Service-Oriented Model-Based Control

TL;DR

This paper introduces a framework for optimizing the computation-accuracy trade-off in Service-Oriented Model-Based Control by formulating service orchestration as an A* search problem and tuning it with Bayesian Optimization, demonstrated on vehicle velocity control.

Contribution

It presents a novel framework combining control and software structure optimization using A* search and Bayesian Optimization for real-time control system reconfiguration.

Findings

Effective online reconfiguration of control architecture demonstrated

Framework balances computation and accuracy in real-time

Vehicle control case study validates approach

Abstract

Representing a control system as a Service-Oriented Architecture (SOA)-referred to as Service-Oriented Model-Based Control (SOMC)-enables runtime-flexible composition of control loop elements. This paper presents a framework that optimizes the computation-accuracy trade-off by formulating service orchestration as an A$^\star$search problem, complemented by Contextual Bayesian Optimization (BO) to tune the multi-objective cost weights. A vehicle longitudinal-velocity control case study demonstrates online, performancedriven reconfiguration of the control architecture. We show that our framework not only combines control and software structure but also considers the real-time requirements of the control system during performance optimization.