Mechanical and Aerospace Engineering
CPS: Synergy: Triggered Control of Cyber Physical Systems with Communication Channels Constraints
NSF, Division of Computer and Network Systems (Sankar Basu)
NSF Award CNS-1446891
Cyber physical systems extend the range of human capabilities in an increasing number of areas with high societal and economic impact, such as smart energy, intelligent transportation, advanced manufacturing, health technology, and the environment. Their successful operation requires the close integration of communication, sensing, actuation, control, and computation. However, advances in these fields have not always been well coordinated. Information theory, for instance, studies how to compress and protect information communicating over noisy channels, while in many control applications communication is abstracted as being instantaneous and reliable. Information theory states that long codes are desirable to protect data against channel noise, but for control applications long delays are not acceptable. On the other hand, triggered control takes an opportunistic approach to decide when actions should be taken to make the system operate efficiently, but largely ignores the constraints imposed by communication. This proposal contributes to the development of a common theoretical framework for control and communication that merges information theory and triggered control to design robust and efficient protocols for the operation of cyber physical systems in real-world scenarios. Such a synergy can have a tremendous impact in the societal settings mentioned above, and at the same time will enable education of students and researchers to prepare themselves in this emerging area of technology. The aim of the project is to develop a synergistic approach to solving the problem of control under communication constraints and/or unreliable communication channels. The approaches to state-triggered control and information-theoretic control individually address different and somewhat complementary aspects of the problem. Therefore, by leveraging the strengths of the two approaches superior and more complete solutions to the problem may be designed. An information-theoretic approach to providing data rate theorems can be used to enrich state-triggered strategies to prescribe both when and what to transmit, as well as to quantify the average usage of the communication channel. Similarly, existing control strategies for unreliable and stochastic communication channels can be enriched by considering triggering mechanisms as additional communication constraints to be accounted for in the feedback loop while designing the communication channel.
CPS: Breakthrough: Robust Team-Triggered Coordination for
Real-Time Control of Networked Cyber-Physical Systems
NSF, Division of Computer and Network Systems (David Corman)
NSF Award CNS-1329619
The aim of this project is to lay down the foundations of a novel approach to real-time control of networked cyber-physical systems (CPS) that leverages their cooperative nature. Most networked controllers are not implementable over embedded digital computer systems because they rely on continuous time or synchronous executions that are costly to enforce. These assumptions are unrealistic when faced with the cyber-physical world, where the interaction between computational and physical components is multiplex, information acquisition is subject to error and delay, and agent schedules are asynchronous. Even without implementation obstacles, the periodic availability of information leads to a wasteful use of resources. Tuning controller execution to the task at hand offers the potential for great savings in communication, sensing, and actuation. The goal of this project is to bring this opportunity to fruition by combining event- and self-triggered control ideas into a unified approach that inherits the best of both models. The key conceptual novelty is for agents to make promises to one another about their future states and warn each other if they later decide to break them. The information provided by promises allows agents to autonomously determine when fresh information is needed, resulting in an efficient network performance.
Self-Triggered Coordination of Robotic Networks
NSF, Division of Electrical, Communications and Cyber Systems (Radhakisan S. Baheti)
NSF Award ECCS-1307176
The objective of this project is the design of self-triggered coordination strategies that account for uncertainty in the state of other agents and the environment, and are able to produce substantial energy savings in the network operation. The key conceptual novelty is the study of how the performance of the overall network task is affected by the quality of the information available to the agents. This understanding leads to tools and triggering criteria for individual agents that allow them to autonomously decide when they need fresh information to successfully perform the required task. Self-triggered strategies eliminate the need for continuous communication, sensing, and re-planning, incorporate uncertainty at the control design stage, seamlessly handle asynchronous executions of plans, and increase agent autonomy and network efficiency. The results of this project will help design robust and efficient cooperative strategies that naturally account for uncertainty and are able to produce substantial energy savings in the network operation. The educational activities are integrated into the research plan.
Robust Distributed Online Convex Optimization
NSF, Division of Civil, Mechanical, and Manufacturing Innovation (Jordan Berg)
NSF Award CMMI-1300272
This project will investigate distributed algorithms for online optimization over networked multi-agent systems. Online optimization refers to the best use of limited agent resources in scenarios where information is dynamic, not a priori available, and increasingly revealed over time. An underlying assumption of present online optimization approaches is the availability of information at a central location. This assumption becomes problematic in networked scenarios, where information is distributed among agents. Transmitting all data to a central location might be costly or inefficient, and raises privacy concerns and the possibility of information leakage. The research will result in the design of robust, distributed strategies that can deal with multiple sources of disruption present in real-world applications. The research approach progresses from the synthesis of online distributed algorithms via saddle-point dynamics to the development of rigorous mathematical analysis with provably correct guarantees. Deliverables include a catalog of provably correct online distributed strategies, novel concepts and tools for the evaluation of performance and complexity, demonstration and validation via computer simulations, documentation of research results, and engineering student education.
Distributed formation control strategies for science imaging
NASA UARC Aligned Research Program
NASA Award TO.030.MM.D
The objective of this proposal is to design and validate distributed control strategies for multiple spacecraft formation flying. NASA has identified multi-interferometer formation flying as an advanced technology critical to searching for new worlds and life outside our solar system. This research proposes to develop formation control algorithms that provide the spacecraft ensemble with formation initialization and tracking capabilities to target distant Earth-sized planets. The innovative technical approach relies on tools from distributed algorithms, emergent behaviors, automata-theoretic methods, nonsmooth optimization and invariance principles.
MRI: Development of an Autonomous Robotic Vehicle Instrument
NSF, Division of Computer and Network Systems (Rita V. Rodriguez)
NSF Award CNS-0521675
This project, developing a platform for research and training to serve as an instrument for evaluating robotic subsystem and supersystem performance, accelerates and enhances the evolution of autonomous vehicle subsystems and component parts by providing a baseline instrument to measure and assess the performance and capabilities of the various portions of a given autonomous vehicle design. This Autonomous Robotic Vehicle Instrument (ARVIN) is designed to allow simultaneous, parallel operation of multiple instruments, actuators, and software/hardware subsystems. Due to the common environment in which the items are operating, the ARVIN yields a precise, robust metric of comparative performance and capability of the item in question. Inherently rapidly reconfigurable by design of its network-centric architecture, the ARVIN enables quick, easy substitutions and augmentations of sensors and actuators on the evaluation platform.
Information-driven distributed coordination of mobile sensor
networks in dynamic scenarios
NSF, Division of Electrical, Communications and Cyber Systems (Radhakisan S. Baheti)
NSF CAREER Award ECS-0830601
Mobile sensor networks hold the promise to provide the rich, in-situ spatio-temporal data needed to revolutionize the detection, estimation, and monitoring of dynamic natural phenomena. Controlled mobility integrated with distributed data fusion capabilities will enable sensor networks to provide broad spatial coverage, react to short-lived events in real time, and track key processes that occur away from fixed sites. The major objective of this project is the synthesis of scalable coordination algorithms for mobile networks performing spatially-distributed sensing tasks. Distributed strategies that maximize the information content of collected data will allow future sensor networks to adapt to changing conditions in a rapid, autonomous and optimal fashion. To make this vision a reality, this project addresses the distributed, in-situ aggregation of data collected by mobile networks in dynamic scenarios, and the information-driven, scalable coordination of the network mobility to optimally perform the required sensing tasks.
The control landscape of selective cell death
NSF, Division of Computer and Communication Foundations (Pinaki Mazumder)
NSF Award CCF-0829891
This project investigates novel computational methods and interventions that might alleviate the suffering caused by complex diseases. Our disease model is the selective killing of cancer cells, but the algorithms developed might also have more general uses for the therapy of other complex diseases. Emerging biological computing paradigms require control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks and thus as information processing. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to incomplete knowledge of the systems biology of cells. In this project, we design algorithms that identify optimal control parameters in cellular networks based on a quantitative characterization of control landscapes, maximizing utilization of incomplete knowledge of the state and structure of intracellular networks. We apply our methods to the control of signal processing that leads to the life/death decision of a cell. In many applications, this control has to be selective. For example, the response to a cytotoxic therapy targeted at cancer cells should ideally occur with minimal response in the normal cells. We define this desired response as selective cell death. The use of new technology for high-throughput measurements, which only recently has become available to academic researchers, is key to this research and essential for the characterization of control landscapes and implementation of the algorithms.
NetSE: Small: Collaborative Research: A Geometric
Computational Approach to Efficiently Deploy and Manage
Self-Organizing Wireless Communication Networks
NSF, Division of Computer and Communication Foundations (Eun K. Park)
NSF Award CCF-0917166
This project will develop efficient mechanisms for deploying and managing wireless self-organizing networks (WSONs). These networks are able to manage themselves with little or no human intervention and consequently can be deployed in remote, difficult-to-access areas, under adverse conditions, and/or when users have little or no network administration skills. As such, WSONs can have significant societal and scientific impact as key enablers of numerous applications, including emergency response, disaster relief, community networking, environmental monitoring, and surveillance. The project will develop theoretical foundations for efficient WSON node placement and trajectory control based on geometric computation and optimization.
DynSyst_Special_Topics: Couplings, Network Dynamics, and Stability of Multi-Agent Systems
NSF, Division of Civil, Mechanical, and Manufacturing Innovation (Eduardo Misawa)
NSF Award CMMI-0908508
This project will develop mathematical tools to analyze the stability of distributed coordination algorithms for complex engineered systems. The project seeks to understand the effects on stability of directed information flows (when the transmittal or acquisition of information is nonsymmetric across the network) and switching interconnection topologies (when changing neighboring relationships induce discontinuities in the dynamic evolution of the network). The results from the project will help engineers design autonomous and adaptive networks in a variety of scenarios, including disaster recovery, environmental monitoring, and ocean sampling.
CDI Type-II: Distributed Ocean Monitoring via Integrated
Data Analysis of Coordinated Buoyancy Drogues
NSF, Division of Ocean Sciences (Kandace S. Binkley)
NSF Award OCE-0941692
An unanswered need in oceanography is to sample the ocean at higher-resolution spatial and temporal scales than presently possible. Although current systems have led to many important discoveries, oceanographers would agree that many fundamental processes are presently unobservable due to~the sparseness of the sampling geometries. This project will develop an original oceanographic observatory system based on small, inexpensive, buoyancy controlled drogues capable of scientific data analysis and coordinated motion control within the shear layers of the ocean circulation to monitor flows of nutrients, behaviors of animals, coastal circulation, and pollution dispersion. Our multidisciplinary team has a collaborative track record that combines the unique expertise of the Scripps Institution of Oceanography (SIO), the Cymer Center for Control Systems and Dynamics (CCSD), and the San Diego Supercomputer Center (SDSC).