282 research outputs found
Finite-horizon optimal control of linear and a class of nonlinear systems
Traditionally, optimal control of dynamical systems with known system dynamics is obtained in a backward-in-time and offline manner either by using Riccati or Hamilton-Jacobi-Bellman (HJB) equation. In contrast, in this dissertation, finite-horizon optimal regulation has been investigated for both linear and nonlinear systems in a forward-in-time manner when system dynamics are uncertain. Value and policy iterations are not used while the value function (or Q-function for linear systems) and control input are updated once a sampling interval consistent with standard adaptive control. First, the optimal adaptive control of linear discrete-time systems with unknown system dynamics is presented in Paper I by using Q-learning and Bellman equation while satisfying the terminal constraint. A novel update law that uses history information of the cost to go is derived. Paper II considers the design of the linear quadratic regulator in the presence of state and input quantization. Quantization errors are eliminated via a dynamic quantizer design and the parameter update law is redesigned from Paper I. Furthermore, an optimal adaptive state feedback controller is developed in Paper III for the general nonlinear discrete-time systems in affine form without the knowledge of system dynamics. In Paper IV, a NN-based observer is proposed to reconstruct the state vector and identify the dynamics so that the control scheme from Paper III is extended to output feedback. Finally, the optimal regulation of quantized nonlinear systems with input constraint is considered in Paper V by introducing a non-quadratic cost functional. Closed-loop stability is demonstrated for all the controller designs developed in this dissertation by using Lyapunov analysis while all the proposed schemes function in an online and forward-in-time manner so that they are practically viable --Abstract, page iv
A unified 3D phase diagram of growth induced surface instabilities
Biological world metabolizes itself with germination, growth, development, and aging every second. A variety of fascinating morphological patterns arise on surfaces of growing, developing or aging tissues, organs and micro--organism colonies. The basic mechanism has been long believed to be the mechanical mismatch due to -differential growth between layers with different biological compositions. These patterns have been observed in separate systems and topologically classified as crease, wrinkle-fold, period-double, ridge, delaminated-buckle, and coexistence states. However, a general and systematic understanding of their initiation and evolution remains largely elusive. We construct a unified 3D phase diagram that predicts initially flat tissue layers can transform to various instability patterns, systematically depending on three physical parameters: mismatch strain, modulus ratio between layers, and adhesion energy on the interface. Our phase diagram matches consistently with our mimic in vitro experiments and documented data in state-of-the-art literature
Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection
We present a novel deep learning framework named the Iteratively Optimized
Patch Label Inference Network (IOPLIN) for automatically detecting various
pavement diseases that are not solely limited to specific ones, such as cracks
and potholes. IOPLIN can be iteratively trained with only the image label via
the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD)
strategy, and accomplish this task well by inferring the labels of patches from
the pavement images. IOPLIN enjoys many desirable properties over the
state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet.
It is able to handle images in different resolutions, and sufficiently utilize
image information particularly for the high-resolution ones, since IOPLIN
extracts the visual features from unrevised image patches instead of the
resized entire image. Moreover, it can roughly localize the pavement distress
without using any prior localization information in the training phase. In
order to better evaluate the effectiveness of our method in practice, we
construct a large-scale Bituminous Pavement Disease Detection dataset named
CQU-BPDD consisting of 60,059 high-resolution pavement images, which are
acquired from different areas at different times. Extensive results on this
dataset demonstrate the superiority of IOPLIN over the state-of-the-art image
classification approaches in automatic pavement disease detection. The source
codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT
Designing bioinspired ondemand displays by electroactivating mechanochemically responsive elastomers
Cephalopods display dazzling colors by locally contracting skin muscles that reversibly activate chromatophores pigments. Inspired by this bioluminescent strategy, we demonstrate a new on-demand display by selectively activating a mechanochemically responsive elastomer controlled by external electric fields. The mechanoresponsive elastomer covalently embedded with mechanochromic molecules, if loaded with sufficiently large force, can reversibly emit visible color and strong fluorescent signals. Upon this reactive elastomer, we employ a controlled electric field to trigger a self-assembled topological pattern that features patterned large deformation, hence displaying a fluorescent pattern. The fluorescent intensity can be predicted by analyzing three-dimensional deformation of the reactive elastomer. We demonstrate on-demand displays such as self-assembled fluorescent rings and lines, and other arbitrary geometries such as letters. The reported technique may pave ways for creating next generation optoelectronics, biomedical luminescent devices, dynamic camouflage coatings, and photoelastic elastomer for damage detection
Assume/Guarantee Contracts for Dynamical Systems:Theory and Computational Tools
Modern engineering systems include many components of different types and
functions. Verifying that these systems satisfy given specifications can be an
arduous task, as most formal verification methods are limited to systems of
moderate size. Recently, contract theory has been proposed as a modular
framework for defining specifications. In this paper, we present a contract
theory for discrete-time dynamical control systems relying on assume/guarantee
contracts, which prescribe assumptions on the input of the system and
guarantees on the output. We then focus on contracts defined by linear
constraints, and develop efficient computational tools for verification of
satisfaction and refinement based on linear programming. We exemplify these
tools in a simulation example, proving safety for a two-vehicle autonomous
driving setting.Comment: 7 pages, 5 figure
A Self-attention Knowledge Domain Adaptation Network for Commercial Lithium-ion Batteries State-of-health Estimation under Shallow Cycles
Accurate state-of-health (SOH) estimation is critical to guarantee the
safety, efficiency and reliability of battery-powered applications. Most SOH
estimation methods focus on the 0-100\% full state-of-charge (SOC) range that
has similar distributions. However, the batteries in real-world applications
usually work in the partial SOC range under shallow-cycle conditions and follow
different degradation profiles with no labeled data available, thus making SOH
estimation challenging. To estimate shallow-cycle battery SOH, a novel
unsupervised deep transfer learning method is proposed to bridge different
domains using self-attention distillation module and multi-kernel maximum mean
discrepancy technique. The proposed method automatically extracts
domain-variant features from charge curves to transfer knowledge from the
large-scale labeled full cycles to the unlabeled shallow cycles. The CALCE and
SNL battery datasets are employed to verify the effectiveness of the proposed
method to estimate the battery SOH for different SOC ranges, temperatures, and
discharge rates. The proposed method achieves a root-mean-square error within
2\% and outperforms other transfer learning methods for different SOC ranges.
When applied to batteries with different operating conditions and from
different manufacturers, the proposed method still exhibits superior SOH
estimation performance. The proposed method is the first attempt at accurately
estimating battery SOH under shallow-cycle conditions without needing a
full-cycle characteristic test
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