Automated modeling techniques allow fast prototyping from measurement or simulation data and can facilitate many important application scenarios, such as shortening the time frame from subsystem design to system integration, calibrating models with higher-order effects, and providing protected models without revealing the intellectual properties of the actual design. The automatically generated black-box models require special system properties to ensure stable simulation. Systems that are unable to generate energy must be modeled properly with the passivity property in order to be simulated stably when interconnected within a global physical network.
This thesis presents an algorithm that can automatically generate time-domain nonlinear passive dynamical models via convex optimization. The optimization scheme incorporates our proposed convex constraints as the sufficient conditions for passivity and incremental stability. The generated models are intended to be interconnected in a larger system to enables the hierarchical modeling strategy.
Practical examples include circuit networks and arterial networks of human cardiovascular systems. It is demonstrated that our generated models, when interconnected within a system, can be simulated in a numerically stable way. The system dynamics of the interconnected models can be faithfully reproduced for a range of operations and shows an excellent agreement with a number of system metrics. In addition, it is also shown via these two applications that the proposed modeling technique is applicable to multiple physical domains.
Thesis Supervisor: Prof. Luca Daniel