Justin M. Bradley

Toward Continuous State–Space Regulation of Coupled Cyber–Physical Systems

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.

Optimization and Control of Cyber-Physical Vehicle Systems

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.

Hybrid system modeling and supervisory control of a microgrid

A new approach to designing a microgrid supervisory controller based on a hybrid automaton is described. A microgrid is defined as a small-scale local grid, comprised of various distributed energy resources (DERs), loads, and energy storage systems. Due to the intermittency of renewable generation, microgrid controllers play a vital role maintaining microgrid stability and integrity in case of an energy shortage (surplus) or when subjected to any disturbances. These controllers should be designed to provide proper control of voltage and frequency. Supervisory controllers are high level control systems that perform optimization, faults detection, protection, and energy management based on their observation from the microgrid. Supervisory controllers define set points to the local controllers for DERs and loads. In this paper, the joint continuous and discrete behavior of the supervisory controller is addressed using a hybrid controller. A complete model of the hybrid automaton (HA) is formalized and tested through one case study of a microgrid using DIgSILENT PowerFactory software. According to the results, the controller can thoroughly manage the load flow within and outside the microgrid.

Coupled Cyber–Physical System Modeling and Coregulation of a CubeSat

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 Optimization for Unmanned Aircraft Systems

Future small unmanned aircraft systems will require careful codesign over both physical and cyber elements to maximize total system efficiency. Mission objectives and success of the system as a whole are becoming increasingly dependent on appropriate allocation of computational resources balanced against demands of the physical actuation systems. In this paper, a cooptimization scheme is described that considers tradeoffs between costs associated with the physical actuation effort required for control and the computational effort required to acquire and process incoming information. A small unmanned aircraft system surveillance mission, the visual inspection of a pipeline, is proposed to investigate specifics of cyber–physical cost terms and their tradeoffs. A multidisciplinary cost function minimizes energy and maximizes mission efficiency and effectiveness. Pareto fronts are examined over combinations of competing cyber and physical objectives, and they demonstrate that excluding either cyber or physica...

Aerospace Cyber-Physical Systems Education

Modern Aerospace systems are cyber-physical, comprised of physical components but commanded and controlled by “cyber” (computing and communication) elements. It is widely acknowledged that codesign across cyber and physical elements will provide a better-performing holistic system, but few of today’s engineers have adequate preparation to model, optimize, and simulate both. Even working in teams, “language barriers” are problematic – most Aerospace engineers are not comfortable with object-oriented abstractions and model-based real-time system design principles from computer science. Conversely, computer scientists have little expertise in the linear and nonlinear calculus-based models describing the motion of complex air and space vehicles, not to mention associated algorithms ensuring stable envelope-wide guidance and control. This paper describes the cyber-physical system (CPS) from both perspectives, and then describes grand challenges in research and applications associated with the CPS. Focus is then turned to education. A review of the traditional Aerospace and computer science curricula is provided first, along with a summary of feedback received from the Aerospace industry regarding knowledge and skill needs for new-hires. A review of CPS courses introduced in other programs is provided, comparing and contrasting the different course perspectives on what is “fundamental” to the CPS. Strategies to migrate content of such courses to the undergraduate Aerospace curriculum are discussed.

Georeferenced mosaics for tracking fires using unmanned miniature air vehicles

Fire tracking is an increasingly important area of research aiming to help firefighters more effectively fight fires. In the past, piloted aircraft have been the main source for obtaining fire characteristics from the air. In recent years unmanned air vehicles (UAVs) have become popular for the same purpose. While large UAVs are an effective means of assisting firefighters, they are expensive to purchase and operate. Mini UAVs or MAVs, on the other hand, have become cheap and reliable platforms for surveillance missions. However, due to weight and size constraints, MAVs are often equipped with error-prone sensors, in contrast to large UAVs, resulting in poor-quality georeferencing and geolocating. Techniques to solve this problem in the fire tracking context have focused on fusing information from on-board infrared and color cameras and using various filters to correct single geolocation estimates. However, a fire can be very large and arbitrarily shaped, thereby invalidating the single geolocation point assumption. We aim to solve this problem by producing a Georeferenced Uncertainty Mosaic (GUM) in which size, shape, and geolocation information is shown simultaneously in an easy to understand georeferenced image. The GUM is created by appropriately blurring the infrared images captured by the on-board camera, and using the blurred images as observations in a particle filter.

Particle Filter Based Mosaicking For Tracking Forest Fires

re to re ghters. It provides a means of presenting size, shape, and geolocation information simultaneously. We describe a novel technique to account for noise in pose estimation of the camera by converting it to the image domain. We also introduce a new concept, a Georeferenced Uncertainty Mosaic (GUM), in which we utilize a Sequential Monte Carlo method (a particle lter) to resolve the noise in the image domain and construct a georeferenced mosaic that simultaneously shows size, shape, geolocation, and uncertainty information about the re.

Mission-Aware Cyber-Physical Optimization on a Tabletop Satellite

As digital and physical systems become more tightly integrated, multi-disciplinary design will be necessary to maximize total-system e ciency. Mission objectives and success of the system as a whole are becoming increasingly dependent on appropriate allocation of computational resources balanced against demands of the physical actuation systems. In this paper we adapt and apply a cooptimization scheme considering tradeo s between costs associated with physical actuation e ort required for control and computational e ort required to acquire and process incoming information. We use TableSat, a tabletop satellite, as a real-world testbed to investigate speci cs of cyber-physical cost terms and their tradeo s. A multi-disciplinary cost function minimizes energy and maximizes mission e ciency and e ectiveness. We examine simulated results generated using numerical methods and demonstrate that excluding either cyber or physical cost terms results in reduced performance for the holistic system over the course of the mission. These theoretical results are then veri ed using experimental data from the TableSat platform.

Toward a cyber-physical quadrotor: Characterizing trajectory following performance

An Unmanned Aircraft System (UAS) is a Cyber- Physical System (CPS) in which a host of real-time computational tasks contending for shared resources must be cooperatively managed to provide actuation input for control of the locomotion necessary to obtain mission objectives. Traditionally, control of the UAS is designed assuming a fixed, high sampling rate in order to maintain reliable performance and margins of stability. But emerging methods challenge this design by dynamically allocating resources to computational tasks, thereby affecting control and mission performance. To apply these emerging strategies, a characterization and understanding of the effects of timing on control and trajectory following performance is required. Going beyond traditional control evaluation techniques, in this paper, we characterize the trajectory following performance, timing, and control of a quadrotor UAS under discrete linear quadratic regulator control designed at various sampling rates. We develop a direct relationship between trajectory following performance and the real-time task period (i.e. sampling rate) of the real-time control task allowing future designs to trade off UAS performance and cyber resources at the planning and/or guidance layer. We also introduce new metrics for characterizing cyber-physical quadrotor performance, and lay the groundwork for the application of CPS control methods to quadrotor UASs.