Knowledge Transfer Between Embedded Controllers

Although many Cyber-Physical Systems (CPS) have similarities among themselves, their control systems are often designed from the scratch. As a result, the knowledge of one expert control system does not come into use in designing and improving other types of control systems. In this paper, we explore the problem of knowledge transfer between two embedded control systems - which enables us to design effective and accurate control systems efficiently and at a large scale. To realize this idea, we formally define the problem of transferring knowledge between two linear time-variant systems. We derive necessary conditions for transferring parameters between two scalar systems as well as two high-order systems. We describe the transfer process which constitutes of a parameter update procedure, a convergence test, and a stability test. We derive a closed-form expression to quantify the performance benefit of the proposed technique in terms of the speed of convergence of system parameter adaptation process. In order to demonstrate the efficacy of the proposed technique, we conduct experiments with a real robotic arm as well as a mobile robot simulator. Our results show that the robotic arm learns its system dynamics 3--5 times faster when it uses transferred knowledge from a well-adapted robotic hand. Similarly, with transferred knowledge, the mobile robot navigates successfully to its target location while making 10 times less learning errors.