Teng Zhang - US grants

Automation is being increasingly introduced into every man-made system. The thrust to achieve trustworthy autonomous systems, which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for meeting these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty. In an autonomous system, uncertainties can arise from the operating environment, adversarial attacks, and from within the system. While a lot of work has been done on ensuring safety of systems under standard sensing errors, much less attention has been given on securing it and its sensors from attacks. As such, autonomous cyber-physical systems (CPS), which rely heavily on sensing units for decision making, remain vulnerable to such attacks. Given the fact that the age of autonomous CPS is upon us and their influence is gradually increasing, it becomes an urgent task to develop effective solutions to ensure the security and trustworthiness of autonomous CPS under adversarial attacks. The researchers of this project provide a comprehensive real-time, resource-aware solution for detection and recovery of autonomous CPS from physical and cyber-attacks. This project also includes effort to educate and prepare the community for the potential cyber and physical threats on autonomous CPS.

With the observation that a thorough security certification of autonomous CPS will provide formal evaluation of autonomous CPS, the researchers in this project intend to develop methods to facilitate manufacturers for certifying security solutions. Toward this goal, the researchers will first develop new theories to understand the impact of physical and cyber-attack on system level properties such as controllability, stability, and safety. They will then develop algorithms for detection and recovery of CPS from physical attacks on active sensors. The proposed recovery method will ensure the integrity of sensor measurements when the system is under attack. Furthermore, a new analysis framework will be constructed that uses platform-based design methodology to represent the CPS and verifies it against design metric constraints such as security, timing, resource, and performance. The key contributions of this project towards autonomous CPS security certification include 1) a comprehensive study of relationship between attacks and system-level properties; 2) algorithms and their optimization for detection and automatic recovery of autonomous CPS from attacks; and 3) systematically quantifying impact of security on design metrics.

This grant will focus on fundamental studies on the multistability of three-dimensional (3D) structures for well-controlled, active architectural reconfigurability. Reconfigurable structures can actively change their geometries and thereby their functionalities upon external stimuli (like mechanical forces, magnetic fields, electric fields, hydration, temperature, and pressure). Such smart, stimuli-responsive structures have a diverse range of applications in deployable solar panels, electromagnetic metamaterials, photonics, biomedical devices, soft robotics, metasurfaces, and many others. Most existing reconfiguration mechanisms, however, require persistent external stimuli to maintain the deformed shape. Reconfigurability through harnessing structural instabilities has emerged as a popular and powerful means of designing various multifunctional reconfigurable devices that can maintain their deformed shape without the need for persistent external stimuli. Despite intensive studies, the difficulty in realizing well-controlled architectural reconfigurability has significantly hindered the rational design of reconfigurable structures, especially for those composed of thin films. This research project will focus on understanding the fundamental relationship between the geometry and mechanical properties of 3D thin-film structures and their multistability and identifying the energy-efficient reconfiguration path from one stable state to another. In addition to the research activities, the project will contribute to the education of students at the graduate, undergraduate, and K-12 levels by supporting interdisciplinary doctoral student training, undergraduate research opportunities, and outreach activities to grade 6-9 girls and K-5 students from underrepresented groups.

The objective of this project is to unravel the fundamental mechanics that govern architectural reconfiguration among multistable, symmetric and asymmetric configurations of flexible, three-dimensional (3D) thin-film structures. To achieve this objective, the specific aims of this project include: (1) maximize the energy barrier and eliminate intermediate local minima between stable states of 3D thin-film structures through energy landscape biasing, and (2) minimize the energy cost for shape change from one local minimum state to another, and realize the reconfigurability of 3D thin-film structures through magnetic force control. The research outcomes of the work will significantly advance our knowledge in the mechanics of multistable structures by (i) establishing relations between geometries and material compositions of thin-film structures and the energy landscape of different stable states, and (ii) determining the active forces to efficiently maneuver the structure from one stable state to another following the minimum energy path. In addition, the project will ultimately facilitate the design of well-controlled, smart reconfigurable structures and generate broad impacts on other fields, including physics, materials science, and smart materials and structures.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Manufacturing technologies that employ smart materials have the potential to revitalize American manufacturing in diverse areas, such as aerospace, biomedicine, energy, and healthcare, through creation of devices that can perform dynamic functions that cannot be achieved by any current approach. Gaps in understanding of the design and preparation of smart materials and the effects of materials processing on their properties and functionality has led to limited fabrication paradigms. This work investigates the fundamental science underlying the interaction of programmed temperature-induced strains and the changes in shape upon heating of shape memory polymers. A process termed Programming-via-Printing will enable single-step fabrication of fully 3D, solid or porous devices with uniform or spatially varying functionality from individual, rather than composite, smart materials using 3D printers. This research has the potential not only to promote the progress of science through improved fundamental understanding of manufacturing of smart materials but will advance the national health, prosperity, and welfare by broadly impacting the many fields using smart materials. Improved understanding will bring the manufacturing of complex smart material devices to new application areas. The multi-disciplinary approach and integrated science and engineering education activities will help broaden participation of underrepresented groups in research and democratize and facilitate spread of the technology developed.

The research will use integrated, interdisciplinary experimentation and simulation to contribute in-depth understanding of advanced manufacturing principles for application of shape-memory polymers in 3D printing. Strains programmed in 3D shape-memory polymers during printing can be quantitatively understood, predicted, and controlled to create complex shapes and functions not currently achieved. The research will: study and tune programming of shape-memory during printing; model determinants of shape-memory from synthesis through printing to understand, predict, and control function; and design, characterize, and study in proof-of-concept shape-memory polymer devices that can only be prepared when programmed via printing. These contributions are significant, because they are expected to provide new fundamental manufacturing understanding of the design, development, and modification of shape-memory polymers for 3D printing while also broadly enabling future applications of shape-memory polymers in 3D printing through study of the Programming-via-Printing approach and discovery of new manufacturing phenomena that can only be studied or applied when shape-memory is programmed via printing.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.