Francesco Borrelli - US grants

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The research objective of this Faculty Early Career Development (CAREER) project is to study the systematic analysis and design of distributed controllers that guarantee constraint satisfaction in large scale networked dynamical systems. The project will focus on networked systems where constraints have a fundamental role: their local violation can lead to a global network failure. For such class of systems the project will deliver a theory and algorithms for analyzing and designing in a systematic way distributed controllers which are predictive, model-based and explicitly take into account systems constraints. The concepts of ?model-based predictions?, ?communication of intent? and ?coordination rules?, widely used in social and biological networks, will be the three key elements in the overall research project development which will be used to guarantee global feasibility, stability and robustness of the network.

If successful, the results of this research will provide a set of tools for the systematic analysis and design of distributed controllers with guarantees on performance and constraint satisfaction. This will eliminate the need of a lengthy and expensive trial and error design procedure required for achieving satisfactory performance and minimum constraint violation. Possible pplication areas include power networks, building management, homeland security, defense, transportation and environmental monitoring. Graduate and undergraduate engineering students will benefit through classroom instruction, involvement in the research and the design of an experiment in the PI?s laboratory.

The objective of this research is to study the formal design and verification of advanced vehicle dynamics control systems. The approach is to consider the vehicle-driver-road system as a cyber-physical system (CPS) by focusing on three critical components: (i) the tire-road interaction; (ii) the driver-vehicle interaction; and (iii) the controller design and validation.

Methods for quantifying and estimating the uncertainty of the road friction coefficient by using self-powered wireless sensors embedded in the tire are developed for considering tire-road interaction. Tools for real-time identification of nominal driver behavior and uncertainty bounds by using in-vehicle cameras and body wireless sensors are developed for considering driver-vehicle interaction. A predictive hybrid supervisory control scheme will guarantee that the vehicle performs safely for all possible uncertainty levels. In particular, for controller design and validation, the CPS autonomy level is continuously adapted as a function of human and environment conditions and their uncertainty bounds quantified by considering tire-road and driver-vehicle interaction.

High confidence is critical in all human operated and supervised cyber-physical systems. These include environmental monitoring, telesurgery, power networks, and any transportation CPS. When human and environment uncertainty bounds can be predicted, safety can be robustly guaranteed by a proper controller design and validation. This avoids lengthy and expensive trial and error design procedures and drastically increases their confidence level. Graduate, undergraduate and underrepresented engineering students benefit from this project through classroom instruction, involvement in the research and substantial interaction with industrial partners from the fields of tires, vehicle active safety, and wireless sensors.

This project focuses on the formal design of semi-autonomous automotive Cyber Physical Systems (CPS). Rather than disconnecting the driver from the vehicle, the goal is to obtain a vehicle where the degree of autonomy is continuously changed in real-time as a function of certified uncertainty ranges for driver behavior and environment reconstruction. The highly integrated research plan will advance the science and engineering for CPS by developing methods for (1) reconstructing 3D scenes which incorporate high-level topological and low-level metric information, (2) extracting driver behavioral models from large datasets using geometry, reasoning and inferences, (3) designing provably-safe control schemes which trade-off real-time feasibility and conservatism by using the evidence collected during actual driving.

Assisting humans in controlling complex and safety-critical systems is a global challenge. In order to improve the safety of human-operated CPS we need to provide guarantees in the reconstruction of the environment where the humans and the CPS operate, and to develop control systems that use predictive cognitive models of the human when interacting with the CPS. A successful and integrated research in both areas will impact not only the automotive sector but many other human-operated systems. These include telesurgery, homeland security, assisted rehabilitation, power networks, environmental monitoring, and all transportation CPS. Graduate, undergraduate and underrepresented engineering students will benefit through classroom instruction, involvement in the research and a continuous interaction with industrial partners who are leaders in the field of assisted driving.

This Cyber-Physical Systems project designs and evaluates a foundational information substrate for efficient, agile, model-driven, human-centered building systems. The approach is to develop software-defined buildings, to shatter existing stovepipe architectures, dramatically reduce the effort to add new functions and applications without ?forklift upgrades,? and expand communications and control capabilities beyond a single stand-alone building to enable groups of buildings to behave cooperatively and in cooperation with the energy grid. We investigate how such Software-Defined Buildings can be founded on a flexible, multi-service and open Building Integrated Operating System (BIOS) that allows applications to run reliably in safe, sandboxed environments. It supports sensor and actuator access, access management, metadata, archiving, and discovery, as well as multiple simultaneously executing programs. Building operators retain supervisory management, controlling application separation physically (access different controls), temporally (change controls at different times), informationally (what information leaves the building), and logically (what actions or sequences thereof are allowable). We construct, deploy, and demonstrate the capabilities of a prototype BIOS in the context of university, residential buildings and closely related industrial processes.

Making buildings more efficient, while keeping occupants comfortable, productive, and healthy, is critical to our economy and health. Transforming buildings into agile, human centered cyber-physical systems eliminates waste, while allowing them to be a proactive resource on the electric grid with zero emission renewable supplies. And by providing greater value from the same physical plant, the SDB approach can move beyond cost-to-build and cost-to-operate metrics to broader return-on-investment for new extendable ?future-proof? technologies.

Children affected by neurological conditions (e.g., Cerebral Palsy, Muscular Atrophy, Spina Bifida and Severe head trauma) often develop significant disabilities including impaired motor control. In many cases, walking becomes a non-functional and exhausting skill that demands the use of the aids or the substitution of function, such as wheelchair. This usually cause these children not to acquire locomotion skills, and consequently to lose their independence. However, it is well understood that bipedal locomotion, an essential human characteristic, ensures the best physiological motor pattern acquisition. For this reason, in children with neurological and neuromuscular diseases, independent walking is a significant rehabilitation goal that must be pursued in a specific temporal window due to the plasticity of central nervous system. In other words, children with neurological conditions have a small window of time to acquire locomotion skills through assisted walking rehearsals. The objective of this research work is to create and experimentally validate a set of technologies that form the framework for the development of adaptive, self-balancing, and modular exoskeleton robotics systems for children with neurological disorders. It is our belief that the exoskeleton (and its associated infrastructure) resulting from this research will offer an effective tool to promote locomotion skill acquisition, and in general health, during a critical period in the early life of children with neurological conditions.


This research proposal develops a data-driven human-machine modeling specific to physiological conditions. This creates regression models that predict the user behavior without explicit modeling the complex human musculoskeletal dynamics and motor control mechanism. Additionally this research project formulates a safe adaptive control problem as a model predictive control (MPC) problem. In this method, an optimal input sequence is computed by solving a constrained finite-time optimal control problem where exoskeleton intrusion (input from exoskeleton) is minimized to maximize the user's intent to promote learning. This project further develops a novel approach for stabilizing and preventing fall of the exoskeleton and the child as a whole. This method allows a child wearing an exoskeleton to learn locomotion skills described above with less likelihood of falls. This research project furthermore evaluates the developed technologies in terms of efficiency and efficacy and creates a novel fun game using exoskeleton for children to promote locomotion skills.