This paper assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) for damping low frequency oscillations (LFOs) in a power system embedded with multiple high voltage DC (HVDC) lines. The improved damping is achieved by designing a coordinated widearea damping controller (CWADC) that employs partial state feedback. The design methodology uses a linear matrix inequality (LMI)-based mixed H2=H1 robust control for multiple operating scenarios. To reduce the high computational burden, an enhanced version of selective modal analysis (SMA) is employed that not only reduces the number of required wide-area feedback signals, but also identifies alternate feedback signals. Additionally, the impact of delays on the performance of the control design is investigated. The studies are performed on a 29 machine, 127 bus equivalent model of the Western Electricity Coordinating Council (WECC) system-embedded with three HVDC lines and two wind farms.
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Embedded control system for stimulation-driven exoskeleton
This paper presents the design and deployment of
a modular, portable and inexpensive embedded control system
architecture for the hybrid neuroprosthesis (HNP). It consist of
a suite of custom designed electronic hardware and firmware
to provide wireless connectivity for close-loop control with
mechanical exoskeletal constraints and neural stimulation with
provisions for power assist to restore locomotion functions for
individuals with spinal cord injury (SCI). The design philosophy,
methodology, and implementation are described and discussed
in details. Bench testing and subject experimentation have been
conducted to evaluate the performance of the HNP system. We
conclude that the embedded control system meets the technical
requirements and design criteria, and can thus be considered as
a potential reference design for generic biomedical research and
clinical deployment in the neuroprosthetic and exoskeleton fields.
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- Award ID(s):
- 1739800
- PAR ID:
- 10073903
- Date Published:
- Journal Name:
- International Symposium on Medical Robotics
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Kirigami-engineering has become an avenue for realizing multifunctional metamaterials that tap into the instability landscape of planar surfaces embedded with cuts. Recently, it has been shown that two-dimensional Kirigami motifs can unfurl a rich space of out-of-plane deformations, which are programmable and controllable across spatial scales. Notwithstanding Kirigami’s versatility, arriving at a cut layout that yields the desired functionality remains a challenge. Here, we introduce a comprehensive machine learning framework to shed light on the Kirigami design space and to rationally guide the design and control of Kirigami-based materials from the meta-atom to the metamaterial level. We employ a combination of clustering, tandem neural networks, and symbolic regression analyses to obtain Kirigami that fulfills specific design constraints and inform on their control and deployment. Our systematic approach is experimentally demonstrated by examining a variety of applications at different hierarchical levels, effectively providing a tool for the discovery of shape-shifting Kirigami metamaterials.
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We are witnessing a race to meet the ever-growing computation requirements of emerging AI applications to provide perception and control in autonomous vehicles — e.g., self-driving cars and UAVs. To remain competitive, vendors are packing more processing units (CPUs, programmable logic, GPUs, and hardware accelerators) into next-generation multiprocessor systems-on-a-chip (MPSoC). As a result, modern embedded platforms are achieving new heights in peak computational capacity. Unfortunately, however, the collateral and inevitable increase in complexity represents a major obstacle for the development of correct-by-design safety-critical real-time applications. Due to the ever-growing gap between fast-paced hardware evolution and comparatively slower evolution of real-time operating systems (RTOS), there is a need for real-time oriented full-platform management frameworks to complement traditional RTOS designs. In this work, we propose one such framework, namely the X-Stream framework, for the definition, synthesis, and analysis of real-time workloads targeting state-of-the-art accelerator-augmented embedded platforms. Our X-Stream framework is designed around two cardinal principles. First, computation and data movements are orchestrated to achieve predictability by design. For this purpose, iterative computation over large data chunks is divided into subsequent segments. These segments are then streamed leveraging the three-phase execution model (load, execute and unload). Second, the framework is workflow-centric: system designers can specify their workflow and the necessary code for workflow orchestration is automatically generated. In addition to automating the deployment of user-defined hardware-accelerated workloads, X-Stream supports the deployment of some computation segments on traditional CPUs. Finally, X-Stream allows the definition of real-time partitions. Each partition groups applications belonging to the same criticality level and that share the same set of hardware resources, with support for preemptive priority-driven scheduling. Conversely, freedom from interference for applications deployed in different partitions is guaranteed by design. We provide a full-system implementation that includes RTOS integration and showcase the proposed X-Stream framework on a Xilinx Ultrascale+ platform by focusing on a matrix-multiplication and addition kernel use-case.more » « less
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The prevailing network security measures are often implemented on proprietary appliances that are deployed at fixed network locations with constant capacity. Such a rigid deployment is sometimes necessary, but undermines the flexibility of security services in meeting the demands of emerging applications, such as augmented/virtual reality, autonomous driving, and 5G for industry 4.0, which are provoked by the evolution of connected and smart devices, their heterogeneity, and integration with cloud and edge computing infrastructures. To loosen these rigid security deployments, in this paper, we propose a data-centric SECurity-as-a-Service (SECaaS) framework for elastic deployment and provisioning of security services at the Multi-Access Edge Computing (MEC) infrastructure. In particular, we discuss three security services that are suitable for edge deployment: (i) an intrusion detection and prevention system (IDPS), (ii) an access control enforcement system (ACE), and (iii) a communication anonymization service (CA). We benchmark the common security microservices along with the design and implementation of a proof of concept communication anonymization application.more » « less
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The last decade has seen tremendous advances in the transformation of ubiquitous control, computing and communication platforms that are anytime, anywhere. These platforms allow humans to interact with machines through sensing, control and actuation functions in ways not imaginable a few decades ago. While robust control techniques aim to maintain autonomous system performance in the presence of bounded modeling errors, they are not designed to manage large multiparameter variations and internal component failures that are inevitable during lengthy periods of field deployment. To address the trustworthiness of autonomous systems in the field, we propose a cross-layer error resilience approach in which errors are detected and corrected at appropriate levels of the design (hardware-through software) with the objective of minimizing the latency of error recovery while maintaining high failure coverage. At the control processor level, soft errors in the digital control processor are considered. At the system level, sensor and actuator failures are analyzed. These impairments define the health of the system. A methodology for adapting the control procedure of the autonomous system to compensate for degraded system health is proposed. It is shown how this methodology can be applied to simple linear and nonlinear control systems to maintain system performance in the presence of internal component failures. Experimental results demonstrate the feasibility of the proposed methodology.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISMR.2018.8333294
