ACQUA: Adaptive and cooperative quality-aware control for automotive cyber-physical systems

Abstract

Controllers in cyber-physical systems integrate a design-time behavioral model of the system under design to improve their own quality. In the state-of-the-art control designs, behavioral models of other interacting neighbor systems are also integrated to form a centralized behavioral model and to enable a system-level optimization and control. Although this ideal embedded control design may result in pareto-optimal solutions, it is not scalable to larger number of systems. Moreover, the behavior of the multi-domain physical systems may be too complex for a control designer to model and may dynamically change at run time. In this paper, we propose a novel Adaptive and Cooperative Quality-Aware (ACQUA) control design which addresses these challenges. In this control design, an ACQUA-based controller for the system under design will monitor the quality of the neighbor systems to dynamically learn their behavior. Therefore, it can quickly adapt its control to cooperate with other neighbor controllers for improving the quality of not only itself, but also other neighbor systems. We apply ACQUA to design a cooperative controller for automotive navigation system, motor control unit, and battery management system in an electric vehicle. We use this automotive example to analyze the performance of the design. We show that by using our ACQUA control, we can reach up to 86% improvements achievable by an ideal embedded control design such that energy consumption reduces by 18% and battery capacity loss decreases by 12% compared to the state-of-the-art on average.