arXiv subjects

Hai Lin

Publications and source records attributed to Hai Lin.

At least 109 records · Page 6Linked to original sources

arXiv2022

Robust Approximate Simulation for Hierarchical Control of Piecewise Affine Systems under Bounded Disturbances

Piecewise affine (PWA) systems are widely applied in many practical cases such as the control of nonlinear systems and hybrid dynamics. However, most of the existing PWA control methods have poor scalability with respect to the number of modes and system dimensions and may not be robust to the disturbances in performance. In this paper, we present a robust approximate simulation based control method for PWA systems under bounded external disturbances. First, a lower-dimensional linear system (abstraction) and an associated interface are designed to enable the output of the PWA system (concrete system) to track the output of the abstraction. Then, a Lyapunov-like simulation function is designed to show the boundedness of the output errors between the two systems. Furthermore, the results obtained for linear abstraction are extended to the case that a simpler PWA system is the abstraction. To illustrate the effectiveness of the proposed approach, simulation results are provided for two design examples.

eess.SY↗

arXiv2022

Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics

Seemingly in defiance of basic physics, cats consistently land on their feet after falling. In this paper, we design a controller that lands the Mini Cheetah quadruped robot on its feet as well. Specifically, we explore how trajectory optimization and machine learning can work together to enable highly dynamic bioinspired behaviors. We find that a reflex approach, in which a neural network learns entire state trajectories, outperforms a policy approach, in which a neural network learns a mapping from states to control inputs. We validate our proposed controller in both simulation and hardware experiments, and are able to land the robot on its feet from falls with initial pitch angles between -90 and 90 degrees.

cs.RO↗

arXiv2021

A More Scalable Mixed-Integer Encoding for Metric Temporal Logic

The state-of-the-art in optimal control from timed temporal logic specifications, including Metric Temporal Logic (MTL) and Signal Temporal Logic (STL), is based on Mixed-Integer Convex Programming (MICP). The standard MICP approach is sound and complete, but struggles to scale to long and complex specifications. Drawing on recent advances in trajectory optimization for piecewise-affine systems, we propose a new MICP encoding for finite transition systems that significantly improves scalability to long and complex MTL specifications. Rather than seeking to reduce the number of variables in the MICP, we focus instead on designing an encoding with a tight convex relaxation. This leads to a larger optimization problem, but significantly improves branch-and-bound solver performance. In simulation experiments involving a mobile robot in a grid-world, the proposed encoding can reduce computation times by several orders of magnitude.

eess.SY↗

arXiv2021

idSTLPy: A Python Toolbox for Active Perception and Control

This paper describes a Python toolbox for active perception and control synthesis of probabilistic signal temporal logic (PrSTL) formulas of switched linear systems with additive Gaussian disturbances and measurement noises. We implement a counterexample-guided synthesis strategy that combines Bounded Model Checking, linear programming, and sampling-based motion planning techniques. We illustrate our approach and the toolbox throughout the paper with a motion planning example for a vehicle with noisy localization. The code is available at \url{https://codeocean.com/capsule/0013534/tree}.

eess.SY↗

arXiv2021

Active Perception and Control from PrSTL Specifications

Next-generation intelligent systems must plan and execute complex tasks with imperfect information about their environment. As a result, plans must also include actions to learn about the environment. This is known as active perception. Most active perception algorithms rely on reward or cost functions, which are usually challenging to specify and offer few theoretical guarantees. On the other hand, symbolic control methods can account for complex tasks using temporal logic but often do not deal well with uncertainties. This work combines symbolic control with active perception to achieve complex tasks in a partially observed and noisy control system with hybrid dynamics. Our basic idea is to employ a counterexample-guided-inductive-synthesis approach for control from probabilistic signal temporal logic (PrSTL) specifications. Our proposed algorithm combines bounded model checking (BMC) with sampling-based trajectory synthesis for uncertain hybrid systems. Active perception is inherently built into the framework because PrSTL formulas are defined in the chance domain.

eess.SY↗

arXiv2021

Universal spin-glass behaviour in bulk LaNiO2, PrNiO2 and NdNiO2

Motivated by the recent discovery of superconductivity in infinite-layer nickelate thin films, we report on a synthesis and magnetization study on bulk samples of the parent compounds ${R}$NiO$_{2}$ (${R}$=La, Pr, Nd). The frequency-dependent peaks of the AC magnetic susceptibility, along with remarkable memory effects, characterize spin-glass states. Furthermore, various phenomenological parameters via different spin glass models show strong similarity within these three compounds as well as with other rare-earth metal nickelates. The universal spin-glass behaviour distinguishes the nickelates from the parent compound CaCuO$_{2}$ of cuprate superconductors, which has the same crystal structure and $d^9$ electronic configuration but undergoes a long-range antiferromagnetic order. Our investigations may indicate a distinctly different nature of magnetism and superconductivity in the bulk nickelates than in the cuprates.

cond-mat.supr-con↗

arXiv2021

Distributed Mean-Field Density Estimation for Large-Scale Systems

This work studies how to estimate the mean-field density of large-scale systems in a distributed manner. Such problems are motivated by the recent swarm control technique that uses mean-field approximations to represent the collective effect of the swarm, wherein the mean-field density (especially its gradient) is usually used in feedback control design. In the first part, we formulate the density estimation problem as a filtering problem of the associated mean-field partial differential equation (PDE), for which we employ kernel density estimation (KDE) to construct noisy observations and use filtering theory of PDE systems to design an optimal (centralized) density filter. It turns out that the covariance operator of observation noise depends on the unknown density. Hence, we use approximations for the covariance operator to obtain a suboptimal density filter, and prove that both the density estimates and their gradient are convergent and remain close to the optimal one using the notion of input-to-state stability (ISS). In the second part, we continue to study how to decentralize the density filter such that each agent can estimate the mean-field density based on only its own position and local information exchange with neighbors. We prove that the local density filter is also convergent and remains close to the centralized one in the sense of ISS. Simulation results suggest that the centralized suboptimal density filter is able to generate convergent density estimates, and the local density filter is able to converge and remain close to the centralized filter.

eess.SY↗

arXiv2021

Control Barrier Functions for Singularity Avoidance in Passivity-Based Manipulator Control

Task-space Passivity-Based Control (PBC) for manipulation has numerous appealing properties, including robustness to modeling error and safety for human-robot interaction. Existing methods perform poorly in singular configurations, however, such as when all the robot's joints are fully extended. Additionally, standard methods for constrained task-space PBC guarantee passivity only when constraints are not active. We propose a convex-optimization-based control scheme that provides guarantees of singularity avoidance, passivity, and feasibility. This work paves the way for PBC with passivity guarantees under other types of constraints as well, including joint limits and contact/friction constraints. The proposed methods are validated in simulation experiments on a 7 degree-of-freedom manipulator.

cs.RO↗

arXiv2021

The Stackelberg Equilibrium for One-sided Zero-sum Partially Observable Stochastic Games

Formulating cyber-security problems with attackers and defenders as a partially observable stochastic game has become a trend recently. Among them, the one-sided two-player zero-sum partially observable stochastic game (OTZ-POSG) has emerged as a popular model because it allows players to compete for multiple stages based on partial knowledge of the system. All existing work on OTZ-POSG has focused on the simultaneous move scenario and assumed that one player's actions are private in the execution process. However, this assumption may become questionable since one player's action may be detected by the opponent through deploying action detection strategies. Hence, in this paper, we propose a turn-based OTZ-POSG with the assumption of public actions and investigate the existence and properties of a Stackelberg equilibrium for this game. We first prove the existence of the Stackelberg equilibrium for the one-stage case and show that the one-stage game can be converted into a linear-fractional programming problem and therefore solved by linear programming. For multiple stages, the main challenge is the information leakage issue as the public run-time action reveals certain private information to the opponent and allows the opponent to achieve more rewards in the future. To deal with this issue, we adopt the concept of $ε$-Stackelberg equilibrium and prove that this equilibrium can be achieved for finite-horizon OTZ-POSGs. We propose a space partition approach to solve the game iteratively and show that the value function of the leader is piece-wise linear and the value function of the follower is piece-wise constant for multiple stages. Finally, examples are given to illustrate the space partition approach and show that value functions are piece-wise linear and piece-wise constant.

eess.SY↗

arXiv2021

SPAN: Subgraph Prediction Attention Network for Dynamic Graphs

This paper proposes a novel model for predicting subgraphs in dynamic graphs, an extension of traditional link prediction. This proposed end-to-end model learns a mapping from the subgraph structures in the current snapshot to the subgraph structures in the next snapshot directly, i.e., edge existence among multiple nodes in the subgraph. A new mechanism named cross-attention with a twin-tower module is designed to integrate node attribute information and topology information collaboratively for learning subgraph evolution. We compare our model with several state-of-the-art methods for subgraph prediction and subgraph pattern prediction in multiple real-world homogeneous and heterogeneous dynamic graphs, respectively. Experimental results demonstrate that our model outperforms other models in these two tasks, with a gain increase from 5.02% to 10.88%.

cs.SI↗

arXiv2021

Entanglement and mixed states of Young tableau states in gauge/gravity correspondence

We use entangled multimode coherent states to produce entangled giant graviton states, in the context of gauge/gravity duality. We make a smeared distribution of the entangled multimode coherent states on the circle, or on the five-sphere, in the higher dimensional view. In gauge/gravity duality, we analyze the superposition of giant graviton states, and the entangled pairs of giant graviton states. We map a class of angular distribution functions to unitary operations on the pairs. We also use Young tableau states to construct cat states and qudit states. Various bipartite quantum states involving Young tableau states are analyzed, including micro-macro entangled states. Mixed states of Young tableau states are generated, by using ensemble mixing using angular distribution functions, and also by going through noisy quantum channels. We then produce mixed entangled pair of giant graviton states, by including interaction with the environment and using noisy quantum channels.

hep-th↗

arXiv2021

Plasmon-Exciton Coupling Effect on Plasmon Damping

Plasmon decay via the surface or interface is a critical process for practical energy conversion and plasmonic catalysis. However, the relationship between plasmon damping and the coupling between the plasmon and 2D materials is still unclear. The spectral splitting due to plasmon-exciton interaction impedes the conventional single-particle method to evaluate the plasmon damping rate by the spectral linewidth directly. Here, we investigated the interaction between a single gold nanorod (GNR) and 2D materials using the single-particle spectroscopy method assisted with in situ nanomanipulation technique by comparing scattering intensity and linewidth together. Our approach allows us to indisputably identify that the plasmon-exciton coupling in the GNR-WSe2 hybrid would induce plasmon damping. We can also isolate the contribution between the charge transfer channel and resonant energy transfer channel for the plasmon decay in the GNR-graphene hybrid by comparing that with thin hBN layers as an intermediate medium to block the charge transfer. We find out that the contact layer between the GNR and 2D materials contributes most of the interfacial plasmon damping. These findings contribute to a deep understanding of interfacial excitonic effects on the plasmon and 2D materials hybrid.

physics.optics↗

arXiv2021

Controlled plasmon-enhanced fluorescence by spherical microcavity

A surrounding electromagnetic environment can engineer spontaneous emissions from quantum emitters through the Purcell effect. For instance, a plasmonic antenna can efficiently confine an electromagnetic field and enhance the fluorescent process. In this study, we demonstrate that a photonic microcavity can modulate plasmon-enhanced fluorescence by engineering the local electromagnetic environment. Consequently, we constructed a plasmon-enhanced emitter (PE-emitter), which comprised a nanorod and a nanodiamond, using the nanomanipulation technique. Furthermore, we controlled a polystyrene sphere approaching the PE-emitter and investigated in situ the associated fluorescent spectrum and lifetime. The emission of PE-emitter can be enhanced resonantly at the photonic modes as compared to that within the free spectral range. The spectral shape modulated by photonic modes is independent of the separation between the PS sphere and PE-emitter. The band integral of the fluorescence decay rate can be enhanced or suppressed after the PS sphere couples to the PE-emitters, depending on the coupling strength between the plasmonic antenna and the photonic cavity. These findings can be utilized in sensing and imaging applications.

physics.optics↗

arXiv2021

Distributed Density Filtering for Large-Scale Systems Using Mean-Filed Models

This work studies distributed (probability) density estimation of large-scale systems. Such problems are motivated by many density-based distributed control tasks in which the real-time density of the swarm is used as feedback information, such as sensor deployment and city traffic scheduling. This work is built upon our previous work [1] which presented a (centralized) density filter to estimate the dynamic density of large-scale systems through a novel integration of mean-field models, kernel density estimation (KDE), and infinite-dimensional Kalman filters. In this work, we further study how to decentralize the density filter such that each agent can estimate the global density only based on its local observation and communication with neighbors. This is achieved by noting that the global observation constructed by KDE is an average of the local kernels. Hence, dynamic average consensus algorithms are used for each agent to track the global observation in a distributed way. We present a distributed density filter which requires very little information exchange, and study its stability and optimality using the notion of input-to-state stability. Simulation results suggest that the distributed filter is able to converge to the centralized filter and remain close to it.

eess.SY↗

arXiv2021

Field Estimation using Robotic Swarms through Bayesian Regression and Mean-Field Feedback

Recent years have seen an increased interest in using mean-field density based modelling and control strategy for deploying robotic swarms. In this paper, we study how to dynamically deploy the robots subject to their physical constraints to efficiently measure and reconstruct certain unknown spatial field (e.g. the air pollution index over a city). Specifically, the evolution of the robots' density is modelled by mean-field partial differential equations (PDEs) which are uniquely determined by the robots' individual dynamics. Bayesian regression models are used to obtain predictions and return a variance function that represents the confidence of the prediction. We formulate a PDE constrained optimization problem based on this variance function to dynamically generate a reference density signal which guides the robots to uncertain areas to collect new data, and design mean-field feedback-based control laws such that the robots' density converges to this reference signal. We also show that the proposed feedback law is robust to density estimation errors in the sense of input-to-state stability. Simulations are included to verify the effectiveness of the algorithms.

eess.SY↗

arXiv2021

Superconductivity and charge-density wave formation in lithium intercalated 2H-LixTaS2

We systematically investigated the superconducting properties and the interplay between charge-density-waves (CDW) and superconductivity in lithium-intercalated 2H-TaS2. By gradually increasing the lithium content x, the CDW formation temperature is continuously suppressed, and the onset temperature of superconductivity is increased with a maximum transition temperature Tc = 3.5 K for x = 0.096. The bulk nature of superconductivity is confirmed by a superconducting shielding fraction of the order of unity for this composition. The electronic contribution to the specific heat and Hall resistivity data demonstrate that the CDW weakens with lithium-intercalation, thereby indirectly increasing carrier density and boosting superconductivity. While the sign of the charge carriers in undoped 2H-TaS2 changes from electron-like to hole type near the CDW formation temperature around 75 K, the lithium intercalated LixTaS2 show predominantly hole-type carriers in the CDW phase even for very low lithium contents.

cond-mat.supr-con↗

arXiv2021

Transporting Robotic Swarms via Mean-Field Feedback Control

With the rapid development of AI and robotics, transporting a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for deploying a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed -- individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.

eess.SY↗

arXiv2021

Local phase delay effect on the asymmetric spectroscopy of plasmon-exciton coupling systems

The phase delay of a local electric field, being well-known in plasmonic nanostructures, has seldom been investigated to modulate the plasmon-exciton interaction. Here, with the single-particle spectroscopy method, we experimentally investigate the phase effect in plasmon-exciton coupling systems consisting of monolayer WSe2 and an individual gold nanorod. The local plasmon phase delay is tuned by adopting various nanorods with different resonant energies respective to the exciton. We find that the local plasmon phase delay between the excitons and the plasmonic modes is as equally essential as the amplitude. The phase delay modulates the plasmon-exciton coupling considerably, resulting in an asymmetric spectral line-shape due to the interference behavior. There is an excellent agreement for the phase delay between the numerically calculated near-field phase distribution and the experimental results. The local phase delay can act as an effective way to modulate the properties of plexcitonic coupling at the nanoscale, which may have potential applications in nanoscale sensing, solar energy devices, and enhancing nonlinear processes.

physics.optics↗