A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined.

We propose the application of state-space techniques to develop a novel coupled cyber-physical system (CPS) model and use feedback control to dynamically adjust CPS resource use and performance. We investigate the use of a gain scheduled discrete linear quadratic regulator controller and a forward-propagation Riccati-based controller to handle the discrete-time-varying system. We demonstrate the value of our approach by conducting a disturbance-rejection case study for a small satellite (CubeSat) application in which resources required for attitude control are adjusted in real-time to maximize availability for other computational tasks. We evaluate CPS performance through a set of metrics quantifying physical system error and control effort as well as cyber resource utilization and compare these with traditional fixed-rate optimal control strategies. Results indicate that our proposed coupled CPS model and controller can provide physical system performance similar to fixed-rate optimal control strategies but with less control effort and much less computational utilization.

Cyber-physical system (CPS) research aims to integrate physical and computational models in a manner that outperforms a system in which the two models are kept separate. CPSs can be generated by either folding properties of the physics-based system into a discrete modeling paradigm or vice versa. This paper studies the latter by abstracting execution rate of a real-time feedback control task into a continuous state-space form traditionally employed for physics-based systems. We propose coupling the two models in a linear systems framework and study the impact of this coupling applied to a single degree of freedom second-order oscillator as well as an unstable inverted pendulum, both regulated with an appropriately designed linear quadratic regulator (LQR). Our results illustrate the utility of the proposed abstraction and controller design as a means of coregulating cyber and physical states in real time.