Cyber-physical systems (CPS) are composed of physical systems that affect computations, and vice versa, in a closed loop. By tightly integrating computing with physical systems one can design CPS that are smarter, cheaper, more reliable, efficient and environmentally friendly than systems based on physical design alone. Examples include modern automobiles, aircraft and trains, power systems, medical devices and manufacturing processes.
The key questions for CPS design are what, where, when and how accurate to measure, compute, communicate and store? My team is providing answers to these questions by developing control systems theory and mathematical optimization methods to automatically design the algorithms, computer architecture and physical system at the same time. This co-design process results in a better overall system compared to iterative methods, where sub-systems are independently designed and optimized.
The main technical challenge in CPS co-design is to merge abstractions from physics with computer science: the study of physical systems is based on differential equations, continuous mathematics and analogue data, whereas the study of computing systems is based on logical operations, discrete mathematics and digital data. Furthermore, while a computation is being carried out, time is ticking and the system continues to evolve according to the laws of physics. A designer therefore has to trade off system performance, robustness and physical resources against the timing and accuracy of measurements, communications, computations and model fidelity.
We are therefore developing methods to: (i) understand and exploit the hybrid and real-time nature of CPS, (ii) model and solve the non-smooth and uncertain mathematical optimization problems that result during the co-design process, and (iii) solve constrained, nonlinear optimization algorithms in real-time on embedded and distributed computing systems.